diff --git a/README.md b/README.md index 84d70fb1..c3ef78de 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,7 @@ You can also view our [Kaggle notebooks here](https://github.com/unslothai/noteb | Llama 3.2 (11B) | **Vision** | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | Llama 3.1 (8B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | Llama 3.1 (8B) | Inference | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Inference.ipynb) +| Llama 3.1 (8B) | Tool Calling | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Tool_Calling.ipynb) | Llama 3 (8B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Alpaca.ipynb) | Llama 3 (8B) | ORPO | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb) | Llama 3 (8B) | Ollama | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb) @@ -60,6 +61,7 @@ You can also view our [Kaggle notebooks here](https://github.com/unslothai/noteb | --- | --- | --- | | Qwen 2.5 (7B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | Qwen 2.5 Coder (14B) | Conversational | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_(14B)-Conversational.ipynb) +| Qwen 2.5 (1.5B) | Tool Calling | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(1.5B)-Tool_Calling.ipynb) | Qwen 2 (7B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_(7B)-Alpaca.ipynb) | Qwen 2 VL (7B) | **Vision** | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) @@ -67,8 +69,8 @@ You can also view our [Kaggle notebooks here](https://github.com/unslothai/noteb | Model | Type | Colab Link | | --- | --- | --- | | CodeGemma (7B) | Conversational | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_(7B)-Conversational.ipynb) -| Gemma 2 (2B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(2B)-Alpaca.ipynb) | Gemma 2 (9B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) +| Gemma 2 (2B) | Alpaca | [Open in Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(2B)-Alpaca.ipynb) ### Other notebooks Notebooks | Model | Type | Colab Link | @@ -97,6 +99,7 @@ Click for all our Kaggle notebooks categorized by model: | Llama 3.2 (11B) | **Vision** | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_(11B)-Vision.ipynb&accelerator=nvidiaTeslaT4) | Llama 3.1 (8B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_(8B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) | Llama 3.1 (8B) | Inference | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_(8B)-Inference.ipynb&accelerator=nvidiaTeslaT4) +| Llama 3.1 (8B) | Tool Calling | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_(8B)-Tool_Calling.ipynb&accelerator=nvidiaTeslaT4) | Llama 3 (8B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3_(8B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) | Llama 3 (8B) | ORPO | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3_(8B)-ORPO.ipynb&accelerator=nvidiaTeslaT4) | Llama 3 (8B) | Ollama | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Llama3_(8B)-Ollama.ipynb&accelerator=nvidiaTeslaT4) @@ -124,6 +127,7 @@ Click for all our Kaggle notebooks categorized by model: | --- | --- | --- | | Qwen 2.5 (7B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_(7B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) | Qwen 2.5 Coder (14B) | Conversational | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_(14B)-Conversational.ipynb&accelerator=nvidiaTeslaT4) +| Qwen 2.5 (1.5B) | Tool Calling | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_(1.5B)-Tool_Calling.ipynb&accelerator=nvidiaTeslaT4) | Qwen 2 (7B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_(7B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) | Qwen 2 VL (7B) | **Vision** | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_VL_(7B)-Vision.ipynb&accelerator=nvidiaTeslaT4) @@ -131,8 +135,8 @@ Click for all our Kaggle notebooks categorized by model: | Model | Type | Kaggle Link | | --- | --- | --- | | CodeGemma (7B) | Conversational | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_(7B)-Conversational.ipynb&accelerator=nvidiaTeslaT4) -| Gemma 2 (2B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Gemma2_(2B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) | Gemma 2 (9B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Gemma2_(9B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) +| Gemma 2 (2B) | Alpaca | [Open in Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Gemma2_(2B)-Alpaca.ipynb&accelerator=nvidiaTeslaT4) ### Other notebooks Notebooks | Model | Type | Kaggle Link | @@ -143,7 +147,7 @@ Click for all our Kaggle notebooks categorized by model: - + # ✨ Contributing to Notebooks diff --git a/nb/Kaggle-Llama3.1_(8B)-Tool_Calling.ipynb b/nb/Kaggle-Llama3.1_(8B)-Tool_Calling.ipynb new file mode 100644 index 00000000..87a29437 --- /dev/null +++ b/nb/Kaggle-Llama3.1_(8B)-Tool_Calling.ipynb @@ -0,0 +1,3773 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Gpaavfkxjn4I" + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": "%%capture\n# Normally using pip install unsloth is enough\n\n# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n!pip install --no-deps cut_cross_entropy unsloth_zoo\n!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n!pip install --no-deps unsloth" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QmUBVEnvCDJv", + "outputId": "ecdb1165-d4e1-4026-e535-030f14fe3917" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Llama patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + } + ], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "fourbit_models = [\n", + " \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 15 trillion tokens model 2x faster!\n", + " \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\", # We also uploaded 4bit for 405b!\n", + " \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n", + " \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n", + " \"unsloth/mistral-7b-v0.3-bnb-4bit\", # Mistral v3 2x faster!\n", + " \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n", + " \"unsloth/Phi-3.5-mini-instruct\", # Phi-3.5 2x faster!\n", + " \"unsloth/Phi-3-medium-4k-instruct\",\n", + " \"unsloth/gemma-2-9b-bnb-4bit\",\n", + " \"unsloth/gemma-2-27b-bnb-4bit\", # Gemma 2x faster!\n", + "] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SXd9bTZd1aaL" + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "6bZsfBuZDeCL", + "outputId": "113c510e-c08e-46f0-cfbc-1a2e0e16d470", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth 2025.2.15 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + ] + } + ], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `llama-3.1` we'll use the [user defined custom tools](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#user-defined-custom-tool-calling) template. If you want to use another model and/or template, you'll need to write your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Define system prompt and message delimiters\n", + "system_prompt = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "{functions}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{{\"example_name\": \"example_value\"}}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|>\"\"\"\n", + "\n", + "user_message = \"<|start_header_id|>user<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "assistant_tool_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eom_id|>\"\n", + "tool_response_message = \"<|start_header_id|>ipython<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_continuation_prefix = \"<|start_header_id|>assistant<|end_header_id|> \"\n", + "assistant_continuation_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "function_string_template = \"\"\"Use the function '{name}' to: {description}\\n{schema}\"\"\"" + ], + "metadata": { + "id": "Vw8Ib-_zU9Eq", + "cellView": "form" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@title Util processing functions\n", + "import ast, json\n", + "\n", + "def convert_tool_format(tool):\n", + " func = tool.get(\"function\", {})\n", + " name = func.get(\"name\", \"\")\n", + " description = func.get(\"description\", \"\")\n", + " parameters_a = func.get(\"parameters\", {})\n", + " properties = parameters_a.get(\"properties\", {})\n", + " required_params = parameters_a.get(\"required\", [])\n", + " def map_type(a_type, a_format=None):\n", + " if a_type == \"string\":\n", + " return \"string\"\n", + " elif a_type == \"number\":\n", + " return \"int\"\n", + " elif a_type == \"boolean\":\n", + " return \"bool\"\n", + " return a_type\n", + " parameters_b = {}\n", + " for param, details in properties.items():\n", + " parameters_b[param] = {\n", + " \"param_type\": map_type(details.get(\"type\"), details.get(\"format\")),\n", + " \"description\": details.get(\"description\", \"\"),\n", + " \"required\": param in required_params\n", + " }\n", + " return {\n", + " \"name\": name,\n", + " \"description\": description,\n", + " \"parameters\": parameters_b\n", + " }\n", + "\n", + "def get_function_string(f):\n", + " converted_tool = convert_tool_format(f)\n", + " return function_string_template.format(\n", + " name=converted_tool[\"name\"],\n", + " description=converted_tool[\"description\"],\n", + " schema=json.dumps(converted_tool)\n", + " )\n", + "\n", + "def convert_function_call_format(call):\n", + " func_data = call.get(\"function\", {})\n", + " func_name = func_data.get(\"name\", \"\")\n", + " arguments_str = func_data.get(\"arguments\", \"{}\")\n", + " try:\n", + " arguments_dict = ast.literal_eval(arguments_str)\n", + " except Exception:\n", + " arguments_dict = {}\n", + " arguments_json = json.dumps(arguments_dict)\n", + " return f\"{arguments_json}\"\n", + "\n", + "def process_block(block):\n", + " tool_index = None\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and \"tool_calls\" in msg:\n", + " tool_index = i\n", + " break\n", + " filtered_block = []\n", + " if tool_index is not None:\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and i < tool_index:\n", + " continue\n", + " filtered_block.append(msg)\n", + " else:\n", + " filtered_block = block\n", + " block_context = \"\"\n", + " tool_called = False\n", + " for msg in filtered_block:\n", + " if msg[\"role\"] == \"assistant\":\n", + " if \"tool_calls\" in msg:\n", + " block_context += assistant_tool_message.format(convert_function_call_format(msg[\"tool_calls\"][0]))\n", + " else:\n", + " if tool_called:\n", + " block_context += assistant_continuation_message.format(msg[\"content\"])\n", + " tool_called = False\n", + " else:\n", + " block_context += assistant_message.format(msg[\"content\"])\n", + " elif msg[\"role\"] == \"tool\":\n", + " block_context += tool_response_message.format(msg[\"content\"])\n", + " tool_called = True\n", + " return block_context\n", + "\n", + "def get_formatted_sample(sample):\n", + " functions_string = \"\\n\\n\".join([get_function_string(f) for f in sample.get(\"tools\", [])])\n", + " context = system_prompt.format(functions=functions_string)\n", + " block = []\n", + " for message in sample[\"messages\"]:\n", + " if message[\"role\"] == \"system\":\n", + " continue\n", + " elif message[\"role\"] == \"user\":\n", + " if block:\n", + " context += process_block(block)\n", + " block = []\n", + " context += user_message.format(message[\"content\"])\n", + " else:\n", + " block.append(message)\n", + " if block:\n", + " context += process_block(block)\n", + " return context\n" + ], + "metadata": { + "id": "8vdlWCEoVAN2" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "a260c69c1bc24faaba45c4d96f6ea2f6", + "3ed1d73212784ebf81c2496cff7e1f2a", + "53565e6b78004ef9b447121f84985bb7", + "86edadf55c99416e911ad55d52494038", + "819ba37f02e549b3bd4b3b7b87f56d1f", + "ea72b5a4fcc54096870d16e6ecb0ca3a", + "7f7aeb38c44a4732af38180fcfb1da6c", + "c607cb289d05404386be3c5c87d83836", + "a5f08b69b7e4432e9d983ac974e09b38", + "9b727b7e5455450d94724eec947e3004", + "848e8392e481410badcdccd57d13dee7" + ] + }, + "outputId": "0555f238-be41-4bbf-b0d6-3e94ea231cc9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/112754 [00:00<|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'track_calories' to: Track daily calorie intake\n", + "{\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"meal\": {\"param_type\": \"string\", \"description\": \"The meal for which calories are being tracked\", \"required\": true}, \"calories\": {\"param_type\": \"int\", \"description\": \"The number of calories consumed\", \"required\": true}, \"date\": {\"param_type\": \"string\", \"description\": \"The date for which calories are being tracked\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}<|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "3237e04b476b4142ae8a0309dcdf327a", + "8b94291cab6240c596621923a4bfc213", + "6c3fb60f196a46799685f7cbbb0be28e", + "c259833aa6d148749293cb0bd7849089", + "a32b643a370d4c8faac31cb30aba28dd", + "25cba0c7c3b64666879ea3157d4b43fb", + "e5cf8fa8df7e458881e2fc468cc707e4", + "fe67b32b5edd4808916aa9e63a8c8d9b", + "7b5ba5b5da384cbcb422f945740a755d", + "808f9973de6844b584dd53b630a398e5", + "e1a68f9deb114c0e8da8229edf323926", + "b5b51a206563427eac85fe849439b99f", + "ba61fdc809444053969d58813a608d6d", + "cc40514e343f4f559049b616b2eacc1f", + "7db6511d4abe4e358ad04ae5ec166140", + "e0785da126f547748282d18c4b79fd58", + "19343326b2de49079565b8e64b67e031", + "458d4fb43fd34a54bf4c6afb8dff2849", + "74a06f38b6f54ce6a21ce43edc9f1641", + "2e5ea29454bb462a9d2eaef3aaf4b186", + "be011cc34c674ef9a237858b6ff8f728", + "f3f34dd0896847fc9161d1a158f60f13", + "3b063da052a44e7abfb00a1f52945e2a", + "1e73832bd5ad4d808a719e446c78dec0", + "5e25580d97b34fafa75f9b1dc3d1882f", + "6e7c8c1e2c434f468eb2f412031d4f7c", + "b9b6dc43ef474d3a972f2e4ee6852dca", + "89d0ea94a2314c7a8a88943cf641a649", + "04e79c7f06294ab8a8074e2e2916fa56", + "2f17a530623049b59b1a5197e7f80370", + "40915aa8e4e84dd7a58c0738e707fe1b", + "3efc83359f7b4d99b70d6ab9fa25d23a", + "91fa89bb400741b4a0fe046b1365dc42", + "6cd1b567f37e479eb3d1cdab522dd39d", + "4997c8e41a3e423a9b3ebe0081746931", + "01825c2a354d40799555a990ba73996b", + "8f2fd22e31f4485ba97f2fe38e61c9f0", + "1921af3669174ac087763cdc506ee76d", + "269f717369c64b90b7e80fbb9c1a9997", + "4fb7436f92ee48cdac83c344904dcccf", + "eb7a01cb8f3848eb94734efa0aee1e61", + "4dc4406ad32e4abf8a491fb59006b491", + "87334ad1ff3f4281977968469e37183d", + "ea5cc23294cc474e93840332d604971a" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "3bfafb60-d7f4-45b5-9937-4b3d1d84bc14" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/112754 [00:00" + ], + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " [60/60 20:26, Epoch 0/1]\n", + "
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StepTraining Loss
12.022000
22.147200
32.373700
41.681200
51.539600
61.761500
71.689500
81.524600
91.107300
101.122800
110.948900
120.939600
130.820500
140.788200
150.609800
160.652800
170.639200
180.374600
190.437900
200.420400
210.449600
220.460100
230.490300
240.400400
250.287900
260.439200
270.502500
280.201300
290.442800
300.377000
310.598200
320.460600
330.201500
340.437300
350.341100
360.322100
370.595400
380.289700
390.415600
400.298400
410.492500
420.508200
430.188600
440.425400
450.261800
460.506300
470.411200
480.165600
490.544000
500.265200
510.476600
520.308800
530.237200
540.361200
550.309200
560.375700
570.369600
580.333500
590.573200
600.230900

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "cellView": "form", + "id": "pCqnaKmlO1U9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67e4f04c-5ade-4097-b775-5d6b27a6289a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1263.8918 seconds used for training.\n", + "21.06 minutes used for training.\n", + "Peak reserved memory = 7.467 GB.\n", + "Peak reserved memory for training = 1.951 GB.\n", + "Peak reserved memory % of max memory = 50.655 %.\n", + "Peak reserved memory for training % of max memory = 13.235 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! We'll load the `test` split of our dataset and prepare it to generation.\n", + "\n", + "**[NOTE]** To use the model's tool calling capabilities in a more streamlined way you should use a scaffolding framework such as [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps). For the scope of this demo we will test the model manually.\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "dataset_test = load_dataset(\"madroid/glaive-function-calling-openai\", split = \"test\")\n", + "dataset_test = dataset_test.map(formatting_prompts_func, batched = True,)" + ], + "metadata": { + "id": "5iUqU8oqg1Ij", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "3623ca82f6ef49c285ffde06c2265d47", + "f12f63657f344aaaae37d0b926ef2a67", + "174598c6bb8946bfb288980ecfb41043", + "e57a6673a2b24f61a86952868d19398b", + "03aa9cc5cce4488dbf4d82d62e72af16", + "5c29f4e94b2647f3abadfacaa590e2bb", + "70701b3922a84f0eaaa8eaffeb9787d3", + "2c2099110ef84a89a8eac9b4ac02106a", + "5c38cfd5e75f4ab98d1613f95fcf8733", + "52a792e911f649b9b051ce1578832686", + "2b09ff9587ab4ff5a07a8e46bb49b413" + ] + }, + "outputId": "0a0ab140-af55-4dbc-e2c1-b85e44e2eae9" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/967 [00:00{\"example_name\": \"example_value\"}<|eom_id|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "d87f4648-2361-472f-9bab-3618bc8ed6ca" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "test_sample = dataset_test[128][\"text\"]\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title **User-defined Custom tools**\n", + "import re\n", + "import json\n", + "\n", + "\n", + "# function to parse model's response\n", + "def parse_function_call(s: str):\n", + " # Regex pattern to extract function name and JSON arguments\n", + " match = re.search(r\"(\\{.*?\\})\", s)\n", + "\n", + " if match:\n", + " function_name = match.group(1) # Extract function name\n", + " args_json = match.group(2) # Extract JSON string\n", + " args = json.loads(args_json) # Parse JSON to dictionary\n", + " return function_name, args\n", + " else:\n", + " return None, None\n", + "\n", + "\n", + "# CUSTOM TOOLS\n", + "def calculate_loan_emi(loan_amount: int, interest_rate: int, loan_term: int) -> float:\n", + " monthly_interest_rate = (interest_rate / 100) / 12\n", + "\n", + " if monthly_interest_rate == 0:\n", + " emi = loan_amount / loan_term\n", + " else:\n", + " emi = (loan_amount * monthly_interest_rate * (1 + monthly_interest_rate) ** loan_term) / \\\n", + " ((1 + monthly_interest_rate) ** loan_term - 1)\n", + "\n", + " return round(emi, 2)\n", + "\n", + "\n", + "def calculate_fuel_consumption(distance: int, fuel_efficiency: int) -> float:\n", + " if fuel_efficiency <= 0:\n", + " raise ValueError(\"Fuel efficiency must be greater than zero.\")\n", + "\n", + " fuel_consumed = distance / fuel_efficiency\n", + " return round(fuel_consumed, 2)\n", + "\n", + "\n", + "TOOLS = {\n", + " \"calculate_loan_emi\": calculate_loan_emi,\n", + " \"calculate_fuel_consumption\": calculate_fuel_consumption,\n", + "}" + ], + "metadata": { + "id": "ln4cwSVJqLjh" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "# Parse and execute tool given model output\n", + "function_name, arguments = parse_function_call(output_text)\n", + "\n", + "\n", + "if function_name is not None:\n", + " tool_response = TOOLS[function_name](**arguments)\n", + "\n", + " # Prepare context\n", + " context = test_sample # original input\n", + " # Add tool call and response\n", + " context += assistant_tool_message.format(output_text)\n", + " context += tool_response_message.format(tool_response)\n", + " # Add generation prompt\n", + " context += assistant_continuation_prefix\n", + "\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + " inputs = tokenizer(\n", + " [\n", + " context\n", + " ], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + " print(context)\n", + " print(\"===\"*20)\n", + "\n", + " outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + " output_text_chat = tokenizer.batch_decode(outputs)\n", + " output_text_chat = output_text_chat[0][len(context):]\n", + " print(output_text_chat)" + ], + "metadata": { + "id": "Ww_lGt0_uj82", + "outputId": "0b8d80ea-d017-493f-9509-e62804d8e71d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|><|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "25.0<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "============================================================\n", + " \n", + "\n", + "The fuel consumption for your car would be 25.0 liters.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "6a7c4e8a-3015-4026-d5f2-bf131c5b4953" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " test_sample+assistant_continuation_prefix,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "973e4561-f354-4595-eaf9-1a0f7f18fc4f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "4297ecc7-fac6-4c8e-8e50-6f5887fc9ed0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "test_sample = dataset_test[128][\"text\"]\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + "
\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "a260c69c1bc24faaba45c4d96f6ea2f6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3ed1d73212784ebf81c2496cff7e1f2a", + "IPY_MODEL_53565e6b78004ef9b447121f84985bb7", + "IPY_MODEL_86edadf55c99416e911ad55d52494038" + ], + "layout": "IPY_MODEL_819ba37f02e549b3bd4b3b7b87f56d1f" + } + }, + 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"1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/nb/Kaggle-Qwen2.5_(1.5B)-Tool_Calling.ipynb b/nb/Kaggle-Qwen2.5_(1.5B)-Tool_Calling.ipynb new file mode 100644 index 00000000..5ae2d25a --- /dev/null +++ b/nb/Kaggle-Qwen2.5_(1.5B)-Tool_Calling.ipynb @@ -0,0 +1,8065 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Gpaavfkxjn4I" + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": "%%capture\n# Normally using pip install unsloth is enough\n\n# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n!pip install --no-deps cut_cross_entropy unsloth_zoo\n!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n!pip install --no-deps unsloth" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412, + "referenced_widgets": [ + "71f9cb34387047e0841f3d7143b09eff", + "f77a064e66d5494e8dfee6c9778d55ae", + "9266cda523a047e69398c03acf3ffdc3", + "d3af17a366e447d8993de4112a2f2d8e", + 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"4809c0a204e1454c8b73c77e80b7068e", + "3baa3ec9af974fe095fc521d9c9713e2", + "550748ee134b40599c767f4df0ac1b9c", + "b2bd662585054a549151e487574ef711", + "11cefba987964a2488388567d2224091", + "643f157fad554c258bf6423e16949f50" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "8a3797cd-6a68-4162-a250-3abc1258e33d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Qwen2 patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/1.53G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `qwen-2.5` we'll use the [official template](https://qwen.readthedocs.io/en/latest/framework/function_call.html#hugging-face-transformers). If you want to use another model and/or template, you'll need to write your own data prep. See [this notebook](https://colab.research.google.com/drive/1-1FbzLnx1DWRa8ysx5KUlhvRtaToCbvV?usp=sharing) for a demo with `llama-3.1-8B`.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Process dataset util\n", + "import json\n", + "import random\n", + "from datasets import load_dataset\n", + "import ast\n", + "\n", + "\n", + "# This is our transformation function that creates the new chat format.\n", + "def get_formatted_sample(item):\n", + " # Parse the JSON string from the dataset sample\n", + " sample = item\n", + " tools = sample.get(\"tools\", [])\n", + "\n", + " # new_history will store the final sequence of messages.\n", + " new_history = []\n", + " # pending_assistant is used to merge consecutive assistant messages.\n", + " pending_assistant = None\n", + " # Mapping from tool call id to function name so we can later label the tool response.\n", + " mapping_tool_id_to_function_name = {}\n", + "\n", + " # Process each message in the original sample.\n", + " for msg in sample[\"messages\"]:\n", + " role = msg.get(\"role\")\n", + "\n", + " if role == \"system\":\n", + " # Flush any pending assistant message\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " # Append system message as-is.\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"user\":\n", + " # Flush any pending assistant message before adding a new user message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"assistant\":\n", + " # If we haven't started merging an assistant message yet, start one.\n", + " if pending_assistant is None:\n", + " pending_assistant = {\"role\": \"assistant\", \"content\": \"\", \"tool_calls\": []}\n", + "\n", + " # Merge textual content if present.\n", + " if \"content\" in msg and msg[\"content\"]:\n", + " if pending_assistant[\"content\"]:\n", + " # Append on a new line if already exists.\n", + " pending_assistant[\"content\"] += \"\\n\" + msg[\"content\"]\n", + " else:\n", + " pending_assistant[\"content\"] = msg[\"content\"]\n", + "\n", + " # Process any tool_calls: remove the \"id\" field and record a mapping.\n", + " if \"tool_calls\" in msg:\n", + " for tc in msg[\"tool_calls\"]:\n", + " # Map the id to function name if present.\n", + " if \"id\" in tc:\n", + " mapping_tool_id_to_function_name[tc[\"id\"]] = tc[\"function\"][\"name\"]\n", + " function_name = tc[\"function\"][\"name\"]\n", + " arguments = tc[\"function\"][\"arguments\"]\n", + " if isinstance(arguments, str):\n", + " arguments = ast.literal_eval(arguments)\n", + " pending_assistant[\"tool_calls\"].append({\n", + " \"name\": function_name,\n", + " \"arguments\": arguments\n", + " })\n", + "\n", + " elif role == \"tool\":\n", + " # For tool responses, we expect a tool_call_id that maps back to a tool call.\n", + " tool_call_id = msg.get(\"tool_call_id\")\n", + " function_name = mapping_tool_id_to_function_name.get(tool_call_id, \"\")\n", + " # Create a tool response message in the chat format (role 'user' with the \"name\" set to the function name).\n", + " tool_response = {\n", + " \"role\": \"user\",\n", + " \"name\": function_name,\n", + " \"content\": msg.get(\"content\", \"\")\n", + " }\n", + " # Flush any pending assistant message before appending the tool response.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(tool_response)\n", + "\n", + " else:\n", + " # For any unknown roles, flush and then append as-is.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " # Flush any remaining pending assistant message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + "\n", + " # Now apply the chat template to the reconstructed history.\n", + " context = tokenizer.apply_chat_template(\n", + " new_history,\n", + " tools=tools,\n", + " tokenize=False,\n", + " add_generation_prompt=False,\n", + " )\n", + " return context" + ], + "metadata": { + "id": "8vdlWCEoVAN2", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "4320dca140a04d61be7fcc6b6c365cfd", + "6d16b778adc34ac0bc7954e277971111", + "3eef8d56318b423fb0584a51cb3467f3", + "fe2c33ec611149ec8856ad05a662e65b", + "bd02734c6e31420ebbb4eec37224bb2a", + "c5ac0299f44c4e2c8a2f7bf9da7d6028", + "cb62b2e68a2c44909c042f2591e94fe4", + "ac0ae1380d7d4e58bed5c586a283cef2", + "5be3ad2ca9da45d8944efee9ee143a7e", + "68b5768d28f04c8297e72645135573af", + "10d58a9944cd46aca717b011262a1009", + "6be075556fa5432595199d632142a8fc", + "7ce5b60e4026463ea23effefeca77e68", + "d0b13f82c73142baa769533a8ffc77ab", + "bb84e9ffcafa4204adf4758afc4d0927", + "c1517a0f7df9480e9f478fc889d1eb0f", + "2d73fbc2e8b64721bbd933df0447a6d3", + "7f6a66d72af5404c907d092ceb5659aa", + "1e3da677909f4ec6b38d177a14f74760", + "008a1349c17e4578b41c44ab27f516e0", + "268240e741d04efaaad59952e6af7ae0", + "b21e3063ff704f9683aadf3cfe43b8c5", + "0220656634664a6f833745dde572541a", + "337d6d214a794128a85ca5060c22bb1b", + "1879f7f262504696901619247f577d14", + "4ffcedaba2244a21ab6bd0d4afc99402", + "3c0bc15cf4184ab1b1dc4428e182e40c", + "9bc456a3719e4791b962b05bc6981476", + "9a38cd248e684fa0b0ba0f5cddaf6b8d", + "4b4a215c60114d71b10f71661414cc3f", + "93181ef56e4e4ef9be43c3d3c6f5430d", + "23a81673196e454b9676ea068f813208", + "8b052675f07349a48a52642c604dde2c", + "5c8683503d2d48af8d72874bef8eaf56", + "5f3ca5f33c4b4835a61df6b25724dfe8", + "bb479926e93d48a2af69aaf9885fb84b", + "68fae34fe47146f686aa5b50a4c24da9", + "79a6b359fdca402daf7c02b4db8e5d6f", + "0c1d30dabd4a4868986cdd31646cf2bb", + "7506ccd49a22485594ec4618a899a396", + "c16b0c0ab1a24d18961939898e048de8", + "44da95bb6f734abfaa8d2a943b627643", + "a290dad18a3f4ecc89738f5b634b341d", + "b90cf08b01b84ec7aa06f66b3415bc89", + "f406ea625b374112b946e88bfbaaa6a6", + "4178edaf1af548a7882e9beb2521ecbb", + "ab4fc29e01b842ceb0223e92165d61ae", + "bdedd3b48b0048719da51cd985c2cd62", + "b323d18298e542d0be87ca336b3e5943", + "a426acfcdd524858a54e8fce2e5c62c7", + "3fc6247935404d07802df9ee928834d8", + "b6878b2b2a8f4f258b89c44ed2370221", + "c66e238c7ef943febef3a9cde3f504a0", + "e5e95f737d8c4e7db6da6222e157deab", + "9d2c942db1c746eda133f1ab13392115", + "867f653d38f64c0f972a62bbbcb65b48", + "9155ada59e6440358cea72942b3eb292", + "870767b871554d1b8b3e27e798a63cea", + "d5c479f39fce4bd48a3b2d5121b8af1b", + "a3b93cd9c53f46fca79e592ac8d8dca4", + "165c8fc782ce42068fa4179a8df856da", + "79c7c684e55b4338b9ce4673f7523835", + "0bfa55a53b104c658d50ca69670bb98e", + "bea1b71b56d240979aeb2f31339f39ab", + "295aff4e8e914f49928ae598dfa54b52", + "42d35f13fad1484ca00a0e4b41617f29" + ] + }, + "outputId": "ade588fd-1c58-4415-d9c9-6723b5e243d0" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/7.55k [00:00system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "0a596682a75b48aea7b9fca27354cba7", + "dbdaf11bba5f4ecb8053aef33be4ea5e", + "38a0cc67d05d497bafdaacf80182b7cf", + "1d2c9b5c146e4259ad6b169eb7da4c95", + "4ecadbf8928b449c95b52403d7ead0db", + "e3af50ac84eb40b59c0e7889867e65d8", + "63da8260e7d840799ddcbc7e2a413e13", + "bf1498a050db4489a7e4675839c69e9f", + "742eae1721bc4138996d1793df23ed91", + "b185bcf1ad794b25b58598b7bfb6a958", + "5e5a21a67bba4c4a84bad69171bf6ae5", + "ca4b19eefa2642fcaaacd37e9e5f8245", + "2d50e238e17945b3b41f19e7621437e3", + "52fca72c8e154095948b603bc86c67f5", + "634f19ab27ac4dceb0ef251bbee34d0a", + "e743585e4ee7455dbee07dd0d94f9823", + "8f5bd89c238249558527a5ab49a42063", + "d4665796761e4b33b9b6f5dddc784831", + "82ac76dfabd04cd3a98ac97b9151c242", + "6d849e4c66004f6a97e8000ec48d0c0e", + "d3187505ec0f4ceead04eb5722f9a2e8", + "3af641eb57eb4bf487000e2268396ee7", + "533410b2e86c4939b3037bcbc599d264", + "17db194fd6c846b181d2e774153317db", + "5cd8add8eeec4ece86e41c6456c2ed3b", + "22008837d9dd4ff7be55ed7b0562c9f3", + "b6438b1c95d14c4ead4c2a386b2d864a", + "3de1d193a653435887050b3eb84a3178", + "0f2598a432284228ac8d01c2c3152401", + "2e679c8bf9c34f2b81214e18944dccb6", + "e72c2daf5a1f41f1b2be0d3a7abf6942", + "8a82dd6ebf9e465db6a4381a340ac193", + "29ab9312d3b34395b49635cba322db87", + "498aba1432e142cbaacc0d4fd406c76b", + "11f98171eab04726a4a3c9aef80c2c29", + "01a2c2ba6a854b4ca908aabcd0c06799", + "83664322eb4047ee8dbaa396142d13ca", + "b469e37dde704c74bff8f09c5d31bea0", + "c2bb4df48fd448da9c12e6114704c26c", + "030b7ff93668430e9911a93451e8c2ef", + "7dc831efa549435ab73a8bfdd59330a2", + "b899ec71c79a47b8bc7ad07143fc238b", + "5b27a8a285344774adbfd1561817fc51", + "8e1426f9b75749c5b2dadccb76349fe5" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "a4285d66-750a-4871-cdb3-ea6a53a77329" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/1024 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
11.631300
21.851800
31.406800
41.483500
51.298500
60.871300
71.042400
80.976100
90.873000
100.906900
110.763200
120.825100
130.626800
140.521400
150.618600
160.615600
170.594100
180.475400
190.610600
200.420300
210.477600
220.735600
230.564100
240.234300
250.415800
260.430200
270.455600
280.638500
290.478200
300.673900
310.623100
320.542600
330.481000
340.430300
350.418000
360.544100
370.409500
380.551900
390.561300
400.305600
410.450900
420.591900
430.232700
440.538200
450.448300
460.265100
470.513700
480.613900
490.491800
500.464800
510.376400
520.508700
530.574700
540.402000
550.534500
560.453500
570.623500
580.446400
590.579200
600.321800

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pCqnaKmlO1U9", + "outputId": "fd1b6225-def1-4ef2-d7a7-2f01bfac4bbd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "259.5915 seconds used for training.\n", + "4.33 minutes used for training.\n", + "Peak reserved memory = 3.266 GB.\n", + "Peak reserved memory for training = 1.741 GB.\n", + "Peak reserved memory % of max memory = 22.156 %.\n", + "Peak reserved memory for training % of max memory = 11.811 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model!\n", + "\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "print(dataset[0][\"text\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lO9dA-_Wq9W9", + "outputId": "c651691c-c7f0-4672-8672-982f2ee577f7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|im_start|>system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Tool using inference util\n", + "import re\n", + "\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "def try_parse_tool_calls(content: str):\n", + " \"\"\"Try parse the tool calls.\"\"\"\n", + " tool_calls = []\n", + " offset = 0\n", + " for i, m in enumerate(re.finditer(r\"\\n(.+)?\\n\", content)):\n", + " if i == 0:\n", + " offset = m.start()\n", + " try:\n", + " func = json.loads(m.group(1))\n", + " tool_calls.append({\"type\": \"function\", \"function\": func})\n", + " if isinstance(func[\"arguments\"], str):\n", + " func[\"arguments\"] = json.loads(func[\"arguments\"])\n", + " except json.JSONDecodeError as e:\n", + " print(f\"Failed to parse tool calls: the content is {m.group(1)} and {e}\")\n", + " pass\n", + " if tool_calls:\n", + " if offset > 0 and content[:offset].strip():\n", + " c = content[:offset]\n", + " else:\n", + " c = \"\"\n", + " return {\"role\": \"assistant\", \"content\": c, \"tool_calls\": tool_calls}\n", + " return {\"role\": \"assistant\", \"content\": re.sub(r\"<\\|im_end\\|>$\", \"\", content)}\n" + ], + "metadata": { + "id": "AsF3E3RTes8w" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model determining which tool to call**\n", + "\n", + "We feed the model with a user message and a list of tools. It responds with a tool call in the following format:\n", + "```xml\n", + "Looking into my database... One sec.\n", + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "markdown", + "source": [ + "**In order** to use qwen-2.5's native tool calling with `transformers`, we must define our functions with Python and pass them as a parameter during `apply_chat_template`.\n", + "\n", + "**[NOTE]** to be correctly parsed a tool must have type annotations and a valid docstring." + ], + "metadata": { + "id": "sgZ4aF7bkq-L" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "4359a9a4-89e1-4819-9aec-2c4b892556c6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "#@title Tool Calling inference\n", + "def find_movie_details(title: str):\n", + " \"\"\"Find details about a movie based on its title\n", + " Args:\n", + " title: The title of the movie\n", + "\n", + " Returns:\n", + " dict: A dictionary containing the movie details\n", + " \"\"\"\n", + " if title == \"Inception\":\n", + " return {\"title\": \"Inception\", \"director\": \"Christopher Nolan\", \"release_year\": 2010, \"genre\": \"Science Fiction\", \"rating\": 8.8}\n", + " elif title == \"The Godfather\":\n", + " return {\"title\": \"The Godfather\", \"director\": \"Francis Ford Coppola\", \"release_year\": 1972, \"genre\": \"Crime, Drama\", \"rating\": 9.2}\n", + " else:\n", + " return {}\n", + "\n", + "\n", + "def play_music(genre: str, mood: str) -> None:\n", + " \"\"\"Play music based on user's preferences\n", + " Args:\n", + " genre: The genre of music to play\n", + " mood: The mood of the music to play\n", + " \"\"\"\n", + " pass\n", + "\n", + "\n", + "# easily accessible by name\n", + "function_name = {\n", + " \"find_movie_details\": find_movie_details,\n", + " \"play_music\": play_music,\n", + "}\n", + "\n", + "\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Good morning! What do you know about Inception? Please provide me all your info on this movie.\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response to the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "messages.append(try_parse_tool_calls(output_text))\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "if tool_calls := messages[-1].get(\"tool_calls\", None):\n", + " for tool_call in tool_calls:\n", + " if fn_call := tool_call.get(\"function\"):\n", + " fn_name: str = fn_call[\"name\"]\n", + " fn_args: dict = fn_call[\"arguments\"]\n", + "\n", + " fn_res: str = json.dumps(function_name[fn_name](**fn_args))\n", + "\n", + " messages.append({\n", + " \"role\": \"tool\",\n", + " \"name\": fn_name,\n", + " \"content\": fn_res,\n", + " })\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "\n", + "print(output_text)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3MzCKXBijw9j", + "outputId": "c8f76d88-e89a-4708-97eb-5d8b1bab9d8b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================\n", + "Inception is a science fiction film directed by Christopher Nolan that was released in 2010. It has an average rating of 8.8.<|im_end|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "a50cae76-90d6-4a69-b232-51910eef9343" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"relaxed\"}}\n", + "<|im_end|>\n", + "<|im_start|>system\n", + "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"find_movie_details\", \"description\": \"Find details about a movie based on its title\", \"parameters\": {\"type\": \"object\", \"properties\": {\"title\": {\"type\": \"string\", \"description\": \"The title of the movie\"}}, \"required\": [\"title\"]}}}\n", + "{\"type\": \"function\", \"function\": {\"name\": \"play_music\", \"description\": \"Play music based on user's preferences\", \"parameters\": {\"type\": \"object\", \"properties\": {\"genre\": {\"type\": \"string\", \"description\": \"The genre of music to play\"}, \"mood\": {\"type\": \"string\", \"description\": \"The mood of the music to play\"}}, \"required\": [\"genre\", \"mood\"]}, \"return\": {\"type\": \"null\"}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Please i want to listen some blues. play immediately<|im_end|>\n", + "<|im_start|>assistant\n", + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"happy\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues. play immediately\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "f8e7d3fe-8e4d-49ee-944f-08e70cdc1d87" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous tall tower in Paris?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "One of the most famous and iconic tall towers in Paris is the Eiffel Tower. Standing at 324 meters (1,063 feet) tall, this wrought iron tower is a symbol of the city and a must-see attraction for tourists from all over the world.<|end_of_text|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + "
\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "71f9cb34387047e0841f3d7143b09eff": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f77a064e66d5494e8dfee6c9778d55ae", + "IPY_MODEL_9266cda523a047e69398c03acf3ffdc3", + "IPY_MODEL_d3af17a366e447d8993de4112a2f2d8e" + ], + "layout": "IPY_MODEL_1c74d89dfcc34836bf73953c5e838430" + } + }, + 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\n", + "\n", + "\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "%%capture\nimport os\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth\nelse:\n # Do this only in Colab and Kaggle notebooks! Otherwise use pip install unsloth\n !pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n !pip install --no-deps cut_cross_entropy unsloth_zoo\n !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n !pip install --no-deps unsloth" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300, + "referenced_widgets": [ + "6e3c281f112b4a86af7a3ef95933d221", + "92395f250a154006923aaf9ea0a9c30b", + "f84bfc5390054ec687c157c4d68199a6", + "4228734651ca45e19fc7bda79817f9b3", + "4613edbbec6846edb5b1677c25d542b6", + "d032fe2ba5d647d99026fdade758c0cd", + "e9971d220fe24552a1e9aa299765cfb9", + "2be29a4553ad4dfea8a9bc620c81a3ae", + "94cbb87829d1486899e2ff6325c2ecdf", + "f878c2e00bc240c7b0333cce950080e1", + "6b908368de51428585552dfef6a83088", + "ac5eacaaee8346c080e54ea7a52648a4", + "7981edf408d54d41bbeac42da7492c6b", + "2b848e5a85bc42bc87945fd9ed5db038", + "e88c33f37d6849e0b1a6b41254104cb9", + "33843107b93647b28985bfc37ea781ca", + "76e5410e286a4a5abd6c213a38aa38bb", + "ae7e90a811f94e75997d6a9ed1be8596", + "bb9f3379310d4b04be694996f3137b28", + "75082ba15db445df907f5612976590ae", + "89934c4f26834f15b9889ec36fee3b65", + "e887160635cb4803b9f33845df615ec6", + "1c7bc5fdb7dd4c39af8d4c2c504ec3ed", + "843a27e619534ea8914f9d36386c364b", + "8f5adc70fbf248f2811527f620553be5", + "4ffb4b2f015046fb94c1115ed0397a20", + "5a232ed040f94633a2a374031284c1f6", + "2006be31c09349738e221295bb84939f", + "07055fc12b0841aaa5317f8252b5d347", + "1eb90e686e214122ae763b1b79ae321d", + "7a935956348e47c68fbdf05ddf4752f3", + "8653acb618ad4e76bbf1daa00ea71238", + "e3c3bd9c4c124b0a8c88c83c1fc747d3", + "1bd75ddaf57c4438a4e2c3070b9cef65", + "a3a3ef6d6337403cabea8b23f7c3021b", + "c2ea0a3f01f34ffa8c94ab9b5098e9da", + "68ea1d7cb8274a639b3fb5326f4218c3", + "39fef7b257614a0595f39355fa226b69", + "d125995cc0934239a01ba01b78529f21", + "634ae4c6cfe04673b1cdc9c9cac4cbf9", + "d7f92e8332374313bee87ccd427446a4", + "36799fbcd90d43128620ff98225a825d", + "5310346dd579424fa676b8e8e64790e7", + "0c1835f404db4846bb13b5da8d8f4447", + "29c5b713f07043dda51820523e5c8ff3", + "d7375f0f048841b29a20601c122666e8", + "f433ced9bfcd4a57ba691d3c1caeed08", + "da8ffc70820a48f5a12c6d4b5967015b", + "1a6db9aea6a64ae3aaef51d6265b35b2", + "331f516c7a76456d801bc2a2feb228aa", + "9be9074028da42d39d044a78393a861f", + "51cd9026b1664819a67712996ca97bd5", + "ea55293415ca48a4be97c2e1e4769122", + "8ea52b105a7e44978caca33c0e7e815b", + "3662f1445ef34a50b462e601ed31bb69" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "0a47b925-663d-4543-9c61-994a6302f3c5" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.27.post2. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "X9ggYCSJYKNv" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6e3c281f112b4a86af7a3ef95933d221", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", - "
\n", - "\n", - "\n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", - "
\n", - "\n", - "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", - "\n", - "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + "text/plain": [ + "model.safetensors: 0%| | 0.00/5.70G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "5e8825fb770b41529f2129113cebc4a9", + "0a9dc233674e4096b7a988a5e4ebaf84", + "374fa9beda4042e1bf9a9b13de6e6674", + "e6533d3c91fd4359bc84ffd8e59af5a3", + "f9b01aebcbdc48a585b7942b0ee60a2d", + "d4b3770433bc41818372b7aed243fb31", + "19bcefcc1d874840ae9a9ca983e474b6", + "4011ce9370d74fad857ec8e1e99d314f", + "41f5fed060ad4c8d87b24602b720ef04", + "88fcd51819b5483c9ab22df7ef89ab64", + "e6ffac074f1b476ba2ade11b37732af3", + "98a6716e7438429ea322adb3e3264f91", + "68c686291b50430faeef0de7840e2c4b", + "953625aa1e824f8a8d203197b316b302", + "f899a815142542219bde22ff792fb60c", + "51ca174d26e94b5cb1e895aa3c770655", + "f4519637bb43400a80ce83505101e8a5", + "805676b197c94f5aa45956daa354640b", + "ce9fcc5eff1f460d80b703a4ca32dad1", + "3fef797403d14440afe599a3bf06b626", + "4e7cb8e988114ed4b6fe09ff9f682dff", + "a14bc1c2130842568a5fde6698731e5f", + "85cc6f24cba54563acb5598f54fed7b9", + "80a72037771e4da9be989eefabbc8e76", + "ba68b274c50b44ec9e02642378d271a6", + "f84d2fe4f1c24a34948755abf1f32b7f", + "86511967834f4484a5ec4af387b7d7a9", + "c97c40c2bf2a41a8ae1c75e0a9c8ebff", + "484e507f14424f2b9173595b985f4101", + "68a32b398e1c490393e01befdc260785", + "04cc963133d242779572d2e847fa3d65", + "94730f13e92a4c9aac35c2cfb21fc48c", + "620c0de28ec74f71a021a2be96dccf3a", + "6e1aff64771c402ab070f650562fa4c9", + "0078f897f2174217a307d95d4f9bd775", + "ecdeaab4f8c94d6dade63bb06857c969", + "735b85f0a0e9411cac4d704a504fcfc1", + "8e992e60416145a8b6eed744287ca0fb", + "8c195b5809604905b5e404baa30e8449", + "2247efac4283489bbd228330344388ab", + "e93d063faf984cc4aa51462418d9b57e", + "9d45b9a5de3e4cba9ac35ad2cb187f51", + "e99423a1ed3f4f72886b39368468b7c1", + "5f174718e5974a7cab024d113f662513" + ] }, + "id": "LjY75GoYUCB8", + "outputId": "80d6c3b9-28c2-4ebf-9c57-6a0b77ce82b1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300, - "referenced_widgets": [ - "6e3c281f112b4a86af7a3ef95933d221", - "92395f250a154006923aaf9ea0a9c30b", - "f84bfc5390054ec687c157c4d68199a6", - "4228734651ca45e19fc7bda79817f9b3", - "4613edbbec6846edb5b1677c25d542b6", - "d032fe2ba5d647d99026fdade758c0cd", - "e9971d220fe24552a1e9aa299765cfb9", - "2be29a4553ad4dfea8a9bc620c81a3ae", - "94cbb87829d1486899e2ff6325c2ecdf", - "f878c2e00bc240c7b0333cce950080e1", - "6b908368de51428585552dfef6a83088", - "ac5eacaaee8346c080e54ea7a52648a4", - "7981edf408d54d41bbeac42da7492c6b", - "2b848e5a85bc42bc87945fd9ed5db038", - "e88c33f37d6849e0b1a6b41254104cb9", - "33843107b93647b28985bfc37ea781ca", - "76e5410e286a4a5abd6c213a38aa38bb", - "ae7e90a811f94e75997d6a9ed1be8596", - "bb9f3379310d4b04be694996f3137b28", - "75082ba15db445df907f5612976590ae", - "89934c4f26834f15b9889ec36fee3b65", - "e887160635cb4803b9f33845df615ec6", - "1c7bc5fdb7dd4c39af8d4c2c504ec3ed", - 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"9be9074028da42d39d044a78393a861f", - "51cd9026b1664819a67712996ca97bd5", - "ea55293415ca48a4be97c2e1e4769122", - "8ea52b105a7e44978caca33c0e7e815b", - "3662f1445ef34a50b462e601ed31bb69" - ] - }, - "id": "QmUBVEnvCDJv", - "outputId": "0a47b925-663d-4543-9c61-994a6302f3c5" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5e8825fb770b41529f2129113cebc4a9", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", - " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", - "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", - "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.27.post2. FA2 = False]\n", - " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", - "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6e3c281f112b4a86af7a3ef95933d221", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "model.safetensors: 0%| | 0.00/5.70G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", - " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", - " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", - " lora_alpha = 16,\n", - " lora_dropout = 0, # Supports any, but = 0 is optimized\n", - " bias = \"none\", # Supports any, but = \"none\" is optimized\n", - " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", - " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", - " random_state = 3407,\n", - " use_rslora = False, # We support rank stabilized LoRA\n", - " loftq_config = None, # And LoftQ\n", - ")" + "text/plain": [ + "Generating train split: 0%| | 0/51760 [00:00\n", - "### Data Prep\n", - "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", - "\n", - "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", - "\n", - "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", - "\n", - "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", - "\n", - "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + "text/plain": [ + "Map: 0%| | 0/51760 [00:00\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "3719bf6f9c6a4c6fbef93c5328c11a07", + "03f492b4b56f4d8e80e9395a65058b1b", + "39d9ef9fb35f47119f319f48eb222070", + "3d7cfb33ceaf417e851ac4393c65148b", + "ece66fa2f128456fa2a82b8a28d1211c", + "9695a640b0ff4e91af495bb59548e4b6", + "d4bd5559d4134d64a943d57972c6ef39", + "fbae6e599d1644f39e5d86efa0f9f997", + "00d425bca350451da6400f9f05c4a659", + "6a27d9ad4f064586a87636b10455d15b", + "77f4367616964a01a8c42416f5f4c147" + ] }, + "id": "95_Nn-89DhsL", + "outputId": "29798478-b975-42d3-b32b-020a805cac35" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 145, - "referenced_widgets": [ - "5e8825fb770b41529f2129113cebc4a9", - "0a9dc233674e4096b7a988a5e4ebaf84", - "374fa9beda4042e1bf9a9b13de6e6674", - "e6533d3c91fd4359bc84ffd8e59af5a3", - "f9b01aebcbdc48a585b7942b0ee60a2d", - "d4b3770433bc41818372b7aed243fb31", - "19bcefcc1d874840ae9a9ca983e474b6", - "4011ce9370d74fad857ec8e1e99d314f", - "41f5fed060ad4c8d87b24602b720ef04", - "88fcd51819b5483c9ab22df7ef89ab64", - "e6ffac074f1b476ba2ade11b37732af3", - "98a6716e7438429ea322adb3e3264f91", - "68c686291b50430faeef0de7840e2c4b", - "953625aa1e824f8a8d203197b316b302", - "f899a815142542219bde22ff792fb60c", - "51ca174d26e94b5cb1e895aa3c770655", - "f4519637bb43400a80ce83505101e8a5", - "805676b197c94f5aa45956daa354640b", - "ce9fcc5eff1f460d80b703a4ca32dad1", - "3fef797403d14440afe599a3bf06b626", - "4e7cb8e988114ed4b6fe09ff9f682dff", - "a14bc1c2130842568a5fde6698731e5f", - "85cc6f24cba54563acb5598f54fed7b9", - "80a72037771e4da9be989eefabbc8e76", - "ba68b274c50b44ec9e02642378d271a6", - "f84d2fe4f1c24a34948755abf1f32b7f", - "86511967834f4484a5ec4af387b7d7a9", - "c97c40c2bf2a41a8ae1c75e0a9c8ebff", - "484e507f14424f2b9173595b985f4101", - "68a32b398e1c490393e01befdc260785", - "04cc963133d242779572d2e847fa3d65", - "94730f13e92a4c9aac35c2cfb21fc48c", - "620c0de28ec74f71a021a2be96dccf3a", - "6e1aff64771c402ab070f650562fa4c9", - "0078f897f2174217a307d95d4f9bd775", - "ecdeaab4f8c94d6dade63bb06857c969", - "735b85f0a0e9411cac4d704a504fcfc1", - "8e992e60416145a8b6eed744287ca0fb", - "8c195b5809604905b5e404baa30e8449", - "2247efac4283489bbd228330344388ab", - "e93d063faf984cc4aa51462418d9b57e", - "9d45b9a5de3e4cba9ac35ad2cb187f51", - "e99423a1ed3f4f72886b39368468b7c1", - "5f174718e5974a7cab024d113f662513" - ] - }, - "id": "LjY75GoYUCB8", - "outputId": "80d6c3b9-28c2-4ebf-9c57-6a0b77ce82b1" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3719bf6f9c6a4c6fbef93c5328c11a07", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5e8825fb770b41529f2129113cebc4a9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading readme: 0%| | 0.00/11.6k [00:00\n", - "### Train the model\n", - "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ], + "source": [ + "from trl import SFTTrainer\n", + "from transformers import TrainingArguments\n", + "from unsloth import is_bfloat16_supported\n", + "\n", + "trainer = SFTTrainer(\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " train_dataset = dataset,\n", + " dataset_text_field = \"text\",\n", + " max_seq_length = max_seq_length,\n", + " dataset_num_proc = 2,\n", + " packing = False, # Can make training 5x faster for short sequences.\n", + " args = TrainingArguments(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_steps = 5,\n", + " # num_train_epochs = 1, # Set this for 1 full training run.\n", + " max_steps = 60,\n", + " learning_rate = 2e-4,\n", + " fp16 = not is_bfloat16_supported(),\n", + " bf16 = is_bfloat16_supported(),\n", + " logging_steps = 1,\n", + " optim = \"adamw_8bit\",\n", + " weight_decay = 0.01,\n", + " lr_scheduler_type = \"linear\",\n", + " seed = 3407,\n", + " output_dir = \"outputs\",\n", + " report_to = \"none\", # Use this for WandB etc\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2ejIt2xSNKKp", + "outputId": "d397dd48-304c-4f42-ecbc-d5c9ce14989c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67, - "referenced_widgets": [ - "3719bf6f9c6a4c6fbef93c5328c11a07", - "03f492b4b56f4d8e80e9395a65058b1b", - "39d9ef9fb35f47119f319f48eb222070", - "3d7cfb33ceaf417e851ac4393c65148b", - "ece66fa2f128456fa2a82b8a28d1211c", - "9695a640b0ff4e91af495bb59548e4b6", - "d4bd5559d4134d64a943d57972c6ef39", - "fbae6e599d1644f39e5d86efa0f9f997", - "00d425bca350451da6400f9f05c4a659", - "6a27d9ad4f064586a87636b10455d15b", - "77f4367616964a01a8c42416f5f4c147" - ] - }, - "id": "95_Nn-89DhsL", - "outputId": "29798478-b975-42d3-b32b-020a805cac35" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3719bf6f9c6a4c6fbef93c5328c11a07", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map (num_proc=2): 0%| | 0/51760 [00:00\n", - " \n", - " \n", - " [60/60 07:28, Epoch 0/1]\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StepTraining Loss
11.817600
22.304200
31.689300
41.938200
51.656900
61.621900
71.187100
81.264200
91.101200
101.189500
110.930800
120.959400
130.929400
141.048700
150.892800
160.901400
171.009100
181.256100
191.016500
200.882600
210.940500
221.018500
230.897200
240.991900
251.072000
261.022900
271.044900
280.877800
290.843800
300.887500
310.853400
320.866000
330.983200
340.852200
350.961200
360.856700
370.872300
380.751100
391.081400
401.174400
410.893400
420.977500
430.957100
440.908100
450.915000
460.973400
470.870900
481.196500
490.907500
501.031300
511.015900
520.907900
530.977000
541.154300
550.778000
561.013300
570.886800
580.827500
590.852300
600.896600

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + "

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StepTraining Loss
11.817600
22.304200
31.689300
41.938200
51.656900
61.621900
71.187100
81.264200
91.101200
101.189500
110.930800
120.959400
130.929400
141.048700
150.892800
160.901400
171.009100
181.256100
191.016500
200.882600
210.940500
221.018500
230.897200
240.991900
251.072000
261.022900
271.044900
280.877800
290.843800
300.887500
310.853400
320.866000
330.983200
340.852200
350.961200
360.856700
370.872300
380.751100
391.081400
401.174400
410.893400
420.977500
430.957100
440.908100
450.915000
460.973400
470.870900
481.196500
490.907500
501.031300
511.015900
520.907900
530.977000
541.154300
550.778000
561.013300
570.886800
580.827500
590.852300
600.896600

" ], - "source": [ - "trainer_stats = trainer.train()" + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "pCqnaKmlO1U9", + "outputId": "edf33a96-b12c-4bba-9771-59e18aee707c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pCqnaKmlO1U9", - "outputId": "edf33a96-b12c-4bba-9771-59e18aee707c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "462.7198 seconds used for training.\n", - "7.71 minutes used for training.\n", - "Peak reserved memory = 7.922 GB.\n", - "Peak reserved memory for training = 1.938 GB.\n", - "Peak reserved memory % of max memory = 53.716 %.\n", - "Peak reserved memory for training % of max memory = 13.141 %.\n" - ] - } - ], - "source": [ - "# @title Show final memory and time stats\n", - "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", - "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", - "used_percentage = round(used_memory / max_memory * 100, 3)\n", - "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", - "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", - "print(\n", - " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", - ")\n", - "print(f\"Peak reserved memory = {used_memory} GB.\")\n", - "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", - "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", - "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "462.7198 seconds used for training.\n", + "7.71 minutes used for training.\n", + "Peak reserved memory = 7.922 GB.\n", + "Peak reserved memory for training = 1.938 GB.\n", + "Peak reserved memory % of max memory = 53.716 %.\n", + "Peak reserved memory for training % of max memory = 13.141 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! You can change the instruction and input - leave the output blank!\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kR3gIAX-SM2q", + "outputId": "087c5c13-e946-4c35-e4f2-e07a88f9ac32" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "ekOmTR1hSNcr" - }, - "source": [ - "\n", - "### Inference\n", - "Let's run the model! You can change the instruction and input - leave the output blank!\n", - "\n", - "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + "data": { + "text/plain": [ + "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "e2pEuRb1r2Vg", + "outputId": "b13f5e53-4ca4-4551-dffa-aaa3c514dca4" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kR3gIAX-SM2q", - "outputId": "087c5c13-e946-4c35-e4f2-e07a88f9ac32" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alpaca_prompt = Copied from above\n", - "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", - "inputs = tokenizer(\n", - "[\n", - " alpaca_prompt.format(\n", - " \"Continue the fibonnaci sequence.\", # instruction\n", - " \"1, 1, 2, 3, 5, 8\", # input\n", - " \"\", # output - leave this blank for generation!\n", - " )\n", - "], return_tensors = \"pt\").to(\"cuda\")\n", - "\n", - "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", - "tokenizer.batch_decode(outputs)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Continue the fibonnaci sequence.\n", + "\n", + "### Input:\n", + "1, 1, 2, 3, 5, 8\n", + "\n", + "### Response:\n", + "13, 21, 34, 55, 89, 144<|end_of_text|>\n" + ] + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "upcOlWe7A1vc", + "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "CrSvZObor0lY" - }, - "source": [ - " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "MKX_XKs_BNZR", + "outputId": "f8e7d3fe-8e4d-49ee-944f-08e70cdc1d87" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "e2pEuRb1r2Vg", - "outputId": "b13f5e53-4ca4-4551-dffa-aaa3c514dca4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", - "\n", - "### Instruction:\n", - "Continue the fibonnaci sequence.\n", - "\n", - "### Input:\n", - "1, 1, 2, 3, 5, 8\n", - "\n", - "### Response:\n", - "13, 21, 34, 55, 89, 144<|end_of_text|>\n" - ] - } + "name": "stdout", + "output_type": "stream", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous tall tower in Paris?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "One of the most famous and iconic tall towers in Paris is the Eiffel Tower. Standing at 324 meters (1,063 feet) tall, this wrought iron tower is a symbol of the city and a must-see attraction for tourists from all over the world.<|end_of_text|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "# alpaca_prompt = You MUST copy from above!\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is a famous tall tower in Paris?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + "
\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0078f897f2174217a307d95d4f9bd775": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8c195b5809604905b5e404baa30e8449", + "placeholder": "\u200b", + "style": "IPY_MODEL_2247efac4283489bbd228330344388ab", + "value": "Map:\u2007100%" + } + }, + "00d425bca350451da6400f9f05c4a659": { + 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"from transformers import TextStreamer\n", - "text_streamer = TextStreamer(tokenizer)\n", - "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" - ] + "layout": "IPY_MODEL_39fef7b257614a0595f39355fa226b69" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "uMuVrWbjAzhc" - }, - "source": [ - "\n", - "### Saving, loading finetuned models\n", - "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", - "\n", - "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" - ] + "1c7bc5fdb7dd4c39af8d4c2c504ec3ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_843a27e619534ea8914f9d36386c364b", + "IPY_MODEL_8f5adc70fbf248f2811527f620553be5", + "IPY_MODEL_4ffb4b2f015046fb94c1115ed0397a20" + ], + "layout": "IPY_MODEL_5a232ed040f94633a2a374031284c1f6" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "upcOlWe7A1vc", - "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('lora_model/tokenizer_config.json',\n", - 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"source": [ - "model.save_pretrained(\"lora_model\") # Local saving\n", - "tokenizer.save_pretrained(\"lora_model\")\n", - "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", - "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" - ] + "layout": "IPY_MODEL_1a6db9aea6a64ae3aaef51d6265b35b2" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "AEEcJ4qfC7Lp" - }, - "source": [ - "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" - ] + "2b848e5a85bc42bc87945fd9ed5db038": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bb9f3379310d4b04be694996f3137b28", + "max": 230, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_75082ba15db445df907f5612976590ae", + "value": 230 + } }, - 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Write a response that appropriately completes the request.\n", - "\n", - "### Instruction:\n", - "What is a famous tall tower in Paris?\n", - "\n", - "### Input:\n", - "\n", - "\n", - "### Response:\n", - "One of the most famous and iconic tall towers in Paris is the Eiffel Tower. Standing at 324 meters (1,063 feet) tall, this wrought iron tower is a symbol of the city and a must-see attraction for tourists from all over the world.<|end_of_text|>\n" - ] - } + "2be29a4553ad4dfea8a9bc620c81a3ae": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": 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"model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_03f492b4b56f4d8e80e9395a65058b1b", + "IPY_MODEL_39d9ef9fb35f47119f319f48eb222070", + "IPY_MODEL_3d7cfb33ceaf417e851ac4393c65148b" ], - "source": [ - "if False:\n", - " from unsloth import FastLanguageModel\n", - " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", - " max_seq_length = max_seq_length,\n", - " dtype = dtype,\n", - " load_in_4bit = load_in_4bit,\n", - " )\n", - " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", - "\n", - "# alpaca_prompt = You MUST copy from above!\n", - "\n", - "inputs = tokenizer(\n", - "[\n", - " alpaca_prompt.format(\n", - " \"What is a famous tall tower in Paris?\", # instruction\n", - " \"\", # input\n", - " \"\", # output - leave this blank for generation!\n", - " )\n", - "], return_tensors = \"pt\").to(\"cuda\")\n", - "\n", - "from transformers import TextStreamer\n", - "text_streamer = TextStreamer(tokenizer)\n", - "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" - ] + "layout": "IPY_MODEL_ece66fa2f128456fa2a82b8a28d1211c" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "QQMjaNrjsU5_" - }, - "source": [ - "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." - ] + "374fa9beda4042e1bf9a9b13de6e6674": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4011ce9370d74fad857ec8e1e99d314f", + "max": 11610, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_41f5fed060ad4c8d87b24602b720ef04", + "value": 11610 + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yFfaXG0WsQuE" - }, - "outputs": [], - "source": [ - "if False:\n", - " # I highly do NOT suggest - use Unsloth if possible\n", - " from peft import AutoPeftModelForCausalLM\n", - " from transformers import AutoTokenizer\n", - " model = AutoPeftModelForCausalLM.from_pretrained(\n", - " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", - " load_in_4bit = load_in_4bit,\n", - " )\n", - " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" - ] + "39d9ef9fb35f47119f319f48eb222070": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fbae6e599d1644f39e5d86efa0f9f997", + "max": 51760, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_00d425bca350451da6400f9f05c4a659", + "value": 51760 + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "f422JgM9sdVT" - }, - "source": [ - "### Saving to float16 for VLLM\n", - "\n", - "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." - ] + "39fef7b257614a0595f39355fa226b69": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iHjt_SMYsd3P" - }, - "outputs": [], - "source": [ - "# Merge to 16bit\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", - "\n", - "# Merge to 4bit\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", - "\n", - "# Just LoRA adapters\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" - ] + "3d7cfb33ceaf417e851ac4393c65148b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6a27d9ad4f064586a87636b10455d15b", + "placeholder": "\u200b", + "style": "IPY_MODEL_77f4367616964a01a8c42416f5f4c147", + "value": "\u200751760/51760\u2007[00:50<00:00,\u20071965.57\u2007examples/s]" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "TCv4vXHd61i7" - }, - "source": [ - "### GGUF / llama.cpp Conversion\n", - "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", - "\n", - "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", - "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", - "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", - "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", - "\n", - "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" - ] + "3fef797403d14440afe599a3bf06b626": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "FqfebeAdT073" - }, - "outputs": [], - "source": [ - "# Save to 8bit Q8_0\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", - "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", - "# And change hf to your username!\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", - "\n", - "# Save to 16bit GGUF\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", - "\n", - "# Save to q4_k_m GGUF\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", - "\n", - "# Save to multiple GGUF options - much faster if you want multiple!\n", - "if False:\n", - " model.push_to_hub_gguf(\n", - " \"hf/model\", # Change hf to your username!\n", - " tokenizer,\n", - " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", - " token = \"\",\n", - " )" - ] + "4011ce9370d74fad857ec8e1e99d314f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "XXuIb4A8YKOU" - }, - "source": [ - "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", - "\n", - "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", - "\n", - "Some other links:\n", - "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", - "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", - "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", - "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", - "\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", - "
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\n", + "\n", + "\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": "%%capture\nimport os\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth\nelse:\n # Do this only in Colab and Kaggle notebooks! Otherwise use pip install unsloth\n !pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n !pip install --no-deps cut_cross_entropy unsloth_zoo\n !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n !pip install --no-deps unsloth" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QmUBVEnvCDJv", + "outputId": "ecdb1165-d4e1-4026-e535-030f14fe3917" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Llama patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + } + ], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "fourbit_models = [\n", + " \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 15 trillion tokens model 2x faster!\n", + " \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\", # We also uploaded 4bit for 405b!\n", + " \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n", + " \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n", + " \"unsloth/mistral-7b-v0.3-bnb-4bit\", # Mistral v3 2x faster!\n", + " \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n", + " \"unsloth/Phi-3.5-mini-instruct\", # Phi-3.5 2x faster!\n", + " \"unsloth/Phi-3-medium-4k-instruct\",\n", + " \"unsloth/gemma-2-9b-bnb-4bit\",\n", + " \"unsloth/gemma-2-27b-bnb-4bit\", # Gemma 2x faster!\n", + "] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SXd9bTZd1aaL" + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "6bZsfBuZDeCL", + "outputId": "113c510e-c08e-46f0-cfbc-1a2e0e16d470", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth 2025.2.15 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + ] + } + ], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `llama-3.1` we'll use the [user defined custom tools](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#user-defined-custom-tool-calling) template. If you want to use another model and/or template, you'll need to write your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Define system prompt and message delimiters\n", + "system_prompt = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "{functions}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{{\"example_name\": \"example_value\"}}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|>\"\"\"\n", + "\n", + "user_message = \"<|start_header_id|>user<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "assistant_tool_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eom_id|>\"\n", + "tool_response_message = \"<|start_header_id|>ipython<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_continuation_prefix = \"<|start_header_id|>assistant<|end_header_id|> \"\n", + "assistant_continuation_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "function_string_template = \"\"\"Use the function '{name}' to: {description}\\n{schema}\"\"\"" + ], + "metadata": { + "id": "Vw8Ib-_zU9Eq", + "cellView": "form" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@title Util processing functions\n", + "import ast, json\n", + "\n", + "def convert_tool_format(tool):\n", + " func = tool.get(\"function\", {})\n", + " name = func.get(\"name\", \"\")\n", + " description = func.get(\"description\", \"\")\n", + " parameters_a = func.get(\"parameters\", {})\n", + " properties = parameters_a.get(\"properties\", {})\n", + " required_params = parameters_a.get(\"required\", [])\n", + " def map_type(a_type, a_format=None):\n", + " if a_type == \"string\":\n", + " return \"string\"\n", + " elif a_type == \"number\":\n", + " return \"int\"\n", + " elif a_type == \"boolean\":\n", + " return \"bool\"\n", + " return a_type\n", + " parameters_b = {}\n", + " for param, details in properties.items():\n", + " parameters_b[param] = {\n", + " \"param_type\": map_type(details.get(\"type\"), details.get(\"format\")),\n", + " \"description\": details.get(\"description\", \"\"),\n", + " \"required\": param in required_params\n", + " }\n", + " return {\n", + " \"name\": name,\n", + " \"description\": description,\n", + " \"parameters\": parameters_b\n", + " }\n", + "\n", + "def get_function_string(f):\n", + " converted_tool = convert_tool_format(f)\n", + " return function_string_template.format(\n", + " name=converted_tool[\"name\"],\n", + " description=converted_tool[\"description\"],\n", + " schema=json.dumps(converted_tool)\n", + " )\n", + "\n", + "def convert_function_call_format(call):\n", + " func_data = call.get(\"function\", {})\n", + " func_name = func_data.get(\"name\", \"\")\n", + " arguments_str = func_data.get(\"arguments\", \"{}\")\n", + " try:\n", + " arguments_dict = ast.literal_eval(arguments_str)\n", + " except Exception:\n", + " arguments_dict = {}\n", + " arguments_json = json.dumps(arguments_dict)\n", + " return f\"{arguments_json}\"\n", + "\n", + "def process_block(block):\n", + " tool_index = None\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and \"tool_calls\" in msg:\n", + " tool_index = i\n", + " break\n", + " filtered_block = []\n", + " if tool_index is not None:\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and i < tool_index:\n", + " continue\n", + " filtered_block.append(msg)\n", + " else:\n", + " filtered_block = block\n", + " block_context = \"\"\n", + " tool_called = False\n", + " for msg in filtered_block:\n", + " if msg[\"role\"] == \"assistant\":\n", + " if \"tool_calls\" in msg:\n", + " block_context += assistant_tool_message.format(convert_function_call_format(msg[\"tool_calls\"][0]))\n", + " else:\n", + " if tool_called:\n", + " block_context += assistant_continuation_message.format(msg[\"content\"])\n", + " tool_called = False\n", + " else:\n", + " block_context += assistant_message.format(msg[\"content\"])\n", + " elif msg[\"role\"] == \"tool\":\n", + " block_context += tool_response_message.format(msg[\"content\"])\n", + " tool_called = True\n", + " return block_context\n", + "\n", + "def get_formatted_sample(sample):\n", + " functions_string = \"\\n\\n\".join([get_function_string(f) for f in sample.get(\"tools\", [])])\n", + " context = system_prompt.format(functions=functions_string)\n", + " block = []\n", + " for message in sample[\"messages\"]:\n", + " if message[\"role\"] == \"system\":\n", + " continue\n", + " elif message[\"role\"] == \"user\":\n", + " if block:\n", + " context += process_block(block)\n", + " block = []\n", + " context += user_message.format(message[\"content\"])\n", + " else:\n", + " block.append(message)\n", + " if block:\n", + " context += process_block(block)\n", + " return context\n" + ], + "metadata": { + "id": "8vdlWCEoVAN2" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "a260c69c1bc24faaba45c4d96f6ea2f6", + "3ed1d73212784ebf81c2496cff7e1f2a", + "53565e6b78004ef9b447121f84985bb7", + "86edadf55c99416e911ad55d52494038", + "819ba37f02e549b3bd4b3b7b87f56d1f", + "ea72b5a4fcc54096870d16e6ecb0ca3a", + "7f7aeb38c44a4732af38180fcfb1da6c", + "c607cb289d05404386be3c5c87d83836", + "a5f08b69b7e4432e9d983ac974e09b38", + "9b727b7e5455450d94724eec947e3004", + "848e8392e481410badcdccd57d13dee7" + ] + }, + "outputId": "0555f238-be41-4bbf-b0d6-3e94ea231cc9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/112754 [00:00<|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'track_calories' to: Track daily calorie intake\n", + "{\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"meal\": {\"param_type\": \"string\", \"description\": \"The meal for which calories are being tracked\", \"required\": true}, \"calories\": {\"param_type\": \"int\", \"description\": \"The number of calories consumed\", \"required\": true}, \"date\": {\"param_type\": \"string\", \"description\": \"The date for which calories are being tracked\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}<|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "3237e04b476b4142ae8a0309dcdf327a", + "8b94291cab6240c596621923a4bfc213", + "6c3fb60f196a46799685f7cbbb0be28e", + "c259833aa6d148749293cb0bd7849089", + "a32b643a370d4c8faac31cb30aba28dd", + "25cba0c7c3b64666879ea3157d4b43fb", + "e5cf8fa8df7e458881e2fc468cc707e4", + "fe67b32b5edd4808916aa9e63a8c8d9b", + "7b5ba5b5da384cbcb422f945740a755d", + "808f9973de6844b584dd53b630a398e5", + "e1a68f9deb114c0e8da8229edf323926", + "b5b51a206563427eac85fe849439b99f", + "ba61fdc809444053969d58813a608d6d", + "cc40514e343f4f559049b616b2eacc1f", + "7db6511d4abe4e358ad04ae5ec166140", + "e0785da126f547748282d18c4b79fd58", + "19343326b2de49079565b8e64b67e031", + "458d4fb43fd34a54bf4c6afb8dff2849", + "74a06f38b6f54ce6a21ce43edc9f1641", + "2e5ea29454bb462a9d2eaef3aaf4b186", + "be011cc34c674ef9a237858b6ff8f728", + "f3f34dd0896847fc9161d1a158f60f13", + "3b063da052a44e7abfb00a1f52945e2a", + "1e73832bd5ad4d808a719e446c78dec0", + "5e25580d97b34fafa75f9b1dc3d1882f", + "6e7c8c1e2c434f468eb2f412031d4f7c", + "b9b6dc43ef474d3a972f2e4ee6852dca", + "89d0ea94a2314c7a8a88943cf641a649", + "04e79c7f06294ab8a8074e2e2916fa56", + "2f17a530623049b59b1a5197e7f80370", + "40915aa8e4e84dd7a58c0738e707fe1b", + "3efc83359f7b4d99b70d6ab9fa25d23a", + "91fa89bb400741b4a0fe046b1365dc42", + "6cd1b567f37e479eb3d1cdab522dd39d", + "4997c8e41a3e423a9b3ebe0081746931", + "01825c2a354d40799555a990ba73996b", + "8f2fd22e31f4485ba97f2fe38e61c9f0", + "1921af3669174ac087763cdc506ee76d", + "269f717369c64b90b7e80fbb9c1a9997", + "4fb7436f92ee48cdac83c344904dcccf", + "eb7a01cb8f3848eb94734efa0aee1e61", + "4dc4406ad32e4abf8a491fb59006b491", + "87334ad1ff3f4281977968469e37183d", + "ea5cc23294cc474e93840332d604971a" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "3bfafb60-d7f4-45b5-9937-4b3d1d84bc14" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/112754 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
12.022000
22.147200
32.373700
41.681200
51.539600
61.761500
71.689500
81.524600
91.107300
101.122800
110.948900
120.939600
130.820500
140.788200
150.609800
160.652800
170.639200
180.374600
190.437900
200.420400
210.449600
220.460100
230.490300
240.400400
250.287900
260.439200
270.502500
280.201300
290.442800
300.377000
310.598200
320.460600
330.201500
340.437300
350.341100
360.322100
370.595400
380.289700
390.415600
400.298400
410.492500
420.508200
430.188600
440.425400
450.261800
460.506300
470.411200
480.165600
490.544000
500.265200
510.476600
520.308800
530.237200
540.361200
550.309200
560.375700
570.369600
580.333500
590.573200
600.230900

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "cellView": "form", + "id": "pCqnaKmlO1U9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67e4f04c-5ade-4097-b775-5d6b27a6289a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1263.8918 seconds used for training.\n", + "21.06 minutes used for training.\n", + "Peak reserved memory = 7.467 GB.\n", + "Peak reserved memory for training = 1.951 GB.\n", + "Peak reserved memory % of max memory = 50.655 %.\n", + "Peak reserved memory for training % of max memory = 13.235 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! We'll load the `test` split of our dataset and prepare it to generation.\n", + "\n", + "**[NOTE]** To use the model's tool calling capabilities in a more streamlined way you should use a scaffolding framework such as [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps). For the scope of this demo we will test the model manually.\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "dataset_test = load_dataset(\"madroid/glaive-function-calling-openai\", split = \"test\")\n", + "dataset_test = dataset_test.map(formatting_prompts_func, batched = True,)" + ], + "metadata": { + "id": "5iUqU8oqg1Ij", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "3623ca82f6ef49c285ffde06c2265d47", + "f12f63657f344aaaae37d0b926ef2a67", + "174598c6bb8946bfb288980ecfb41043", + "e57a6673a2b24f61a86952868d19398b", + "03aa9cc5cce4488dbf4d82d62e72af16", + "5c29f4e94b2647f3abadfacaa590e2bb", + "70701b3922a84f0eaaa8eaffeb9787d3", + "2c2099110ef84a89a8eac9b4ac02106a", + "5c38cfd5e75f4ab98d1613f95fcf8733", + "52a792e911f649b9b051ce1578832686", + "2b09ff9587ab4ff5a07a8e46bb49b413" + ] + }, + "outputId": "0a0ab140-af55-4dbc-e2c1-b85e44e2eae9" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/967 [00:00{\"example_name\": \"example_value\"}<|eom_id|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "d87f4648-2361-472f-9bab-3618bc8ed6ca" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "test_sample = dataset_test[128][\"text\"]\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title **User-defined Custom tools**\n", + "import re\n", + "import json\n", + "\n", + "\n", + "# function to parse model's response\n", + "def parse_function_call(s: str):\n", + " # Regex pattern to extract function name and JSON arguments\n", + " match = re.search(r\"(\\{.*?\\})\", s)\n", + "\n", + " if match:\n", + " function_name = match.group(1) # Extract function name\n", + " args_json = match.group(2) # Extract JSON string\n", + " args = json.loads(args_json) # Parse JSON to dictionary\n", + " return function_name, args\n", + " else:\n", + " return None, None\n", + "\n", + "\n", + "# CUSTOM TOOLS\n", + "def calculate_loan_emi(loan_amount: int, interest_rate: int, loan_term: int) -> float:\n", + " monthly_interest_rate = (interest_rate / 100) / 12\n", + "\n", + " if monthly_interest_rate == 0:\n", + " emi = loan_amount / loan_term\n", + " else:\n", + " emi = (loan_amount * monthly_interest_rate * (1 + monthly_interest_rate) ** loan_term) / \\\n", + " ((1 + monthly_interest_rate) ** loan_term - 1)\n", + "\n", + " return round(emi, 2)\n", + "\n", + "\n", + "def calculate_fuel_consumption(distance: int, fuel_efficiency: int) -> float:\n", + " if fuel_efficiency <= 0:\n", + " raise ValueError(\"Fuel efficiency must be greater than zero.\")\n", + "\n", + " fuel_consumed = distance / fuel_efficiency\n", + " return round(fuel_consumed, 2)\n", + "\n", + "\n", + "TOOLS = {\n", + " \"calculate_loan_emi\": calculate_loan_emi,\n", + " \"calculate_fuel_consumption\": calculate_fuel_consumption,\n", + "}" + ], + "metadata": { + "id": "ln4cwSVJqLjh" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "# Parse and execute tool given model output\n", + "function_name, arguments = parse_function_call(output_text)\n", + "\n", + "\n", + "if function_name is not None:\n", + " tool_response = TOOLS[function_name](**arguments)\n", + "\n", + " # Prepare context\n", + " context = test_sample # original input\n", + " # Add tool call and response\n", + " context += assistant_tool_message.format(output_text)\n", + " context += tool_response_message.format(tool_response)\n", + " # Add generation prompt\n", + " context += assistant_continuation_prefix\n", + "\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + " inputs = tokenizer(\n", + " [\n", + " context\n", + " ], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + " print(context)\n", + " print(\"===\"*20)\n", + "\n", + " outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + " output_text_chat = tokenizer.batch_decode(outputs)\n", + " output_text_chat = output_text_chat[0][len(context):]\n", + " print(output_text_chat)" + ], + "metadata": { + "id": "Ww_lGt0_uj82", + "outputId": "0b8d80ea-d017-493f-9509-e62804d8e71d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|><|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "25.0<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "============================================================\n", + " \n", + "\n", + "The fuel consumption for your car would be 25.0 liters.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "6a7c4e8a-3015-4026-d5f2-bf131c5b4953" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " test_sample+assistant_continuation_prefix,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "973e4561-f354-4595-eaf9-1a0f7f18fc4f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "4297ecc7-fac6-4c8e-8e50-6f5887fc9ed0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "test_sample = dataset_test[128][\"text\"]\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + "
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"1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/nb/Qwen2.5_(1.5B)-Tool_Calling.ipynb b/nb/Qwen2.5_(1.5B)-Tool_Calling.ipynb new file mode 100644 index 00000000..2b2c5a14 --- /dev/null +++ b/nb/Qwen2.5_(1.5B)-Tool_Calling.ipynb @@ -0,0 +1,8065 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Gpaavfkxjn4I" + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": "%%capture\nimport os\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth\nelse:\n # Do this only in Colab and Kaggle notebooks! Otherwise use pip install unsloth\n !pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n !pip install --no-deps cut_cross_entropy unsloth_zoo\n !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n !pip install --no-deps unsloth" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412, + "referenced_widgets": [ + "71f9cb34387047e0841f3d7143b09eff", + "f77a064e66d5494e8dfee6c9778d55ae", + "9266cda523a047e69398c03acf3ffdc3", + "d3af17a366e447d8993de4112a2f2d8e", + "1c74d89dfcc34836bf73953c5e838430", + "de6ea585acfa4e90b8fe6f59e1792f82", + "77fdd891288d43f08efe9c395f1302d1", + "8cb63d8393714fcead327e968e08c361", + "ce851a4c38844d8c94a07555a754801d", + "725597873bba4aea9812d361b3b4d129", + "2de845d9bbb341c18af272c19bdfe888", + "aa502afb522b4abe8bc5026ae84ff23d", + "eeb33fa5c96d42da98d400d8667fc966", + "14a75b40fc1c42af8731dbb45e2cc5df", + "8a12e96711f341879cd4d8d438d5ac33", + "f5212f38391f4aa88a9ac12d81140995", + "ac4f32c14d26415fb1c4645ed460a410", + "574ece7c44084ab6ae42a879878817aa", + "b0d44bf6b9de4969b6ab9cf190aed4dc", + "7474566aa51d45fcabf5c77a9e30d81e", + "c44c8c3792954b3f8af611e2be7d34e7", + "3c70df010ef140bf974507841a2703f7", + "9f348b85be70447b815459150a519736", + "3eae02f8290e4666984216faba0ba2bb", + "b88642c91a46455b974afcde68d00d42", + "8b34d95bd48b48c1a86cbce78c217f10", + "e66961779d8e4271882a9e0598f5f1ac", + "0866f761ffe84485a60cb06dad11aca8", + "e9c434fccae447f09d13fffe18ec41d8", + "c9f285a54f7a48efaa5ad9605708235a", + "09baf2e797ea401e9545103ba8fe6000", + "17f28a06c59248069a49b77506c59ace", + "6c114f3f4fb043efade68a472a8551d4", + "80fed48b85374bc4983331c8114a7d22", + "b99bf6447b4e45f99486cb895eeb6f86", + "e73925b0544a4834a5ee7058c52cca5d", + "1a33bf446e3c4401a17c5520f32108db", + "b9b681560f0f4bc78bfc721f19fd5caf", + "1f51f0424a8748b3b06b440660da3adb", + "a779a401ce114107b2a6bfbcf28311c7", + "e8133c18ab7c43c18b4cc43254e4f0da", + "f8f79092a1494470987bb2316d71d224", + "94b7593fa6174dba9f3ed719d6ccaeb6", + "8a7f8884f7214137bb4d39e6980ed4df", + "76357e6387a2443695adb14e8a5d9737", + "5fd773820b4f4acca8f20aa4d9e4d6b3", + "e42c072ef3884d4b948d5711087598ff", + "3b0cdcc5e6a24e7fbbc02bc634907193", + "8514ccf7d6c94bb0b1d132816bc421bc", + "ab78a055d1c34e01bab8244b8bb756e0", + "b565ab1c9e984f07a07c04a63e555342", + "64de6300d6494da8811abf595830e30d", + "bb7f2744aafe42a290ec94196a88de5e", + "09d8628a8b7d4756a662fcfb4133a6a8", + "7c3f5784417b402b8d95463e2f4d7a7c", + "1ace686ba3434e309f495ecfe1212ba0", + "ba60ba42234948b4bf941fc6a3f5bc4b", + "9c002c0e5ab84106b58f8e862a8f9c27", + "50efff75586041a7a46b03c1d78e3f3a", + "be2f9676162c400abcd42201d2332bb5", + "20230200ada34b94aa597004a0bd72ba", + "cdd56482b03949a1be138f61aed13ca0", + "ab069913d16547d6b835fa83b979e719", + "c1c98e079bfd46139aef6310d01f0bcd", + "57eeedc2747a4ab1acac8c085a3bfd90", + "08a83fc74af645569a033616a403022f", + "0c5b321ae7824b88a02332a96b49a5f3", + "7755d1707f9e4b73bf33f3bcab40041a", + "ec73dc50449440fbace48dcbd2236b36", + "e909ffd5179f46f88a65a13d1b07f5d8", + "1a5436dba19f416b9352ff33e81702c5", + "5c472ec4ee644b47b6cfe67b05d4aee4", + "924eac3620424493846f52c766c7fc0b", + "fa6dfec76bb14e3886221de2f84f0aba", + "4928ed19fb014cb0b498ca52b4172897", + "15b1e342a684417bbcf7d51e6b1a335f", + "72ce42df20ed44e1861efa34ac37d78b", + "ebfe4dd1d38343f5b42dbb502c13efd6", + "9cc3ef5c15494227a102454e76e4c188", + "488daae294bc49a6b77d27e7bfa09367", + "fcbd95560b2a4547ac6f2b8a8c0f79a5", + "343f5ecde6964e3fa5e87cc4745227ab", + "4809c0a204e1454c8b73c77e80b7068e", + "3baa3ec9af974fe095fc521d9c9713e2", + "550748ee134b40599c767f4df0ac1b9c", + "b2bd662585054a549151e487574ef711", + "11cefba987964a2488388567d2224091", + "643f157fad554c258bf6423e16949f50" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "8a3797cd-6a68-4162-a250-3abc1258e33d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Qwen2 patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/1.53G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `qwen-2.5` we'll use the [official template](https://qwen.readthedocs.io/en/latest/framework/function_call.html#hugging-face-transformers). If you want to use another model and/or template, you'll need to write your own data prep. See [this notebook](https://colab.research.google.com/drive/1-1FbzLnx1DWRa8ysx5KUlhvRtaToCbvV?usp=sharing) for a demo with `llama-3.1-8B`.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Process dataset util\n", + "import json\n", + "import random\n", + "from datasets import load_dataset\n", + "import ast\n", + "\n", + "\n", + "# This is our transformation function that creates the new chat format.\n", + "def get_formatted_sample(item):\n", + " # Parse the JSON string from the dataset sample\n", + " sample = item\n", + " tools = sample.get(\"tools\", [])\n", + "\n", + " # new_history will store the final sequence of messages.\n", + " new_history = []\n", + " # pending_assistant is used to merge consecutive assistant messages.\n", + " pending_assistant = None\n", + " # Mapping from tool call id to function name so we can later label the tool response.\n", + " mapping_tool_id_to_function_name = {}\n", + "\n", + " # Process each message in the original sample.\n", + " for msg in sample[\"messages\"]:\n", + " role = msg.get(\"role\")\n", + "\n", + " if role == \"system\":\n", + " # Flush any pending assistant message\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " # Append system message as-is.\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"user\":\n", + " # Flush any pending assistant message before adding a new user message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"assistant\":\n", + " # If we haven't started merging an assistant message yet, start one.\n", + " if pending_assistant is None:\n", + " pending_assistant = {\"role\": \"assistant\", \"content\": \"\", \"tool_calls\": []}\n", + "\n", + " # Merge textual content if present.\n", + " if \"content\" in msg and msg[\"content\"]:\n", + " if pending_assistant[\"content\"]:\n", + " # Append on a new line if already exists.\n", + " pending_assistant[\"content\"] += \"\\n\" + msg[\"content\"]\n", + " else:\n", + " pending_assistant[\"content\"] = msg[\"content\"]\n", + "\n", + " # Process any tool_calls: remove the \"id\" field and record a mapping.\n", + " if \"tool_calls\" in msg:\n", + " for tc in msg[\"tool_calls\"]:\n", + " # Map the id to function name if present.\n", + " if \"id\" in tc:\n", + " mapping_tool_id_to_function_name[tc[\"id\"]] = tc[\"function\"][\"name\"]\n", + " function_name = tc[\"function\"][\"name\"]\n", + " arguments = tc[\"function\"][\"arguments\"]\n", + " if isinstance(arguments, str):\n", + " arguments = ast.literal_eval(arguments)\n", + " pending_assistant[\"tool_calls\"].append({\n", + " \"name\": function_name,\n", + " \"arguments\": arguments\n", + " })\n", + "\n", + " elif role == \"tool\":\n", + " # For tool responses, we expect a tool_call_id that maps back to a tool call.\n", + " tool_call_id = msg.get(\"tool_call_id\")\n", + " function_name = mapping_tool_id_to_function_name.get(tool_call_id, \"\")\n", + " # Create a tool response message in the chat format (role 'user' with the \"name\" set to the function name).\n", + " tool_response = {\n", + " \"role\": \"user\",\n", + " \"name\": function_name,\n", + " \"content\": msg.get(\"content\", \"\")\n", + " }\n", + " # Flush any pending assistant message before appending the tool response.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(tool_response)\n", + "\n", + " else:\n", + " # For any unknown roles, flush and then append as-is.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " # Flush any remaining pending assistant message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + "\n", + " # Now apply the chat template to the reconstructed history.\n", + " context = tokenizer.apply_chat_template(\n", + " new_history,\n", + " tools=tools,\n", + " tokenize=False,\n", + " add_generation_prompt=False,\n", + " )\n", + " return context" + ], + "metadata": { + "id": "8vdlWCEoVAN2", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "4320dca140a04d61be7fcc6b6c365cfd", + "6d16b778adc34ac0bc7954e277971111", + "3eef8d56318b423fb0584a51cb3467f3", + "fe2c33ec611149ec8856ad05a662e65b", + "bd02734c6e31420ebbb4eec37224bb2a", + "c5ac0299f44c4e2c8a2f7bf9da7d6028", + "cb62b2e68a2c44909c042f2591e94fe4", + "ac0ae1380d7d4e58bed5c586a283cef2", + "5be3ad2ca9da45d8944efee9ee143a7e", + "68b5768d28f04c8297e72645135573af", + "10d58a9944cd46aca717b011262a1009", + "6be075556fa5432595199d632142a8fc", + "7ce5b60e4026463ea23effefeca77e68", + "d0b13f82c73142baa769533a8ffc77ab", + "bb84e9ffcafa4204adf4758afc4d0927", + "c1517a0f7df9480e9f478fc889d1eb0f", + "2d73fbc2e8b64721bbd933df0447a6d3", + "7f6a66d72af5404c907d092ceb5659aa", + "1e3da677909f4ec6b38d177a14f74760", + "008a1349c17e4578b41c44ab27f516e0", + "268240e741d04efaaad59952e6af7ae0", + "b21e3063ff704f9683aadf3cfe43b8c5", + "0220656634664a6f833745dde572541a", + "337d6d214a794128a85ca5060c22bb1b", + "1879f7f262504696901619247f577d14", + "4ffcedaba2244a21ab6bd0d4afc99402", + "3c0bc15cf4184ab1b1dc4428e182e40c", + "9bc456a3719e4791b962b05bc6981476", + "9a38cd248e684fa0b0ba0f5cddaf6b8d", + "4b4a215c60114d71b10f71661414cc3f", + "93181ef56e4e4ef9be43c3d3c6f5430d", + "23a81673196e454b9676ea068f813208", + "8b052675f07349a48a52642c604dde2c", + "5c8683503d2d48af8d72874bef8eaf56", + "5f3ca5f33c4b4835a61df6b25724dfe8", + "bb479926e93d48a2af69aaf9885fb84b", + "68fae34fe47146f686aa5b50a4c24da9", + "79a6b359fdca402daf7c02b4db8e5d6f", + "0c1d30dabd4a4868986cdd31646cf2bb", + "7506ccd49a22485594ec4618a899a396", + "c16b0c0ab1a24d18961939898e048de8", + "44da95bb6f734abfaa8d2a943b627643", + "a290dad18a3f4ecc89738f5b634b341d", + "b90cf08b01b84ec7aa06f66b3415bc89", + "f406ea625b374112b946e88bfbaaa6a6", + "4178edaf1af548a7882e9beb2521ecbb", + "ab4fc29e01b842ceb0223e92165d61ae", + "bdedd3b48b0048719da51cd985c2cd62", + "b323d18298e542d0be87ca336b3e5943", + "a426acfcdd524858a54e8fce2e5c62c7", + "3fc6247935404d07802df9ee928834d8", + "b6878b2b2a8f4f258b89c44ed2370221", + "c66e238c7ef943febef3a9cde3f504a0", + "e5e95f737d8c4e7db6da6222e157deab", + "9d2c942db1c746eda133f1ab13392115", + "867f653d38f64c0f972a62bbbcb65b48", + "9155ada59e6440358cea72942b3eb292", + "870767b871554d1b8b3e27e798a63cea", + "d5c479f39fce4bd48a3b2d5121b8af1b", + "a3b93cd9c53f46fca79e592ac8d8dca4", + "165c8fc782ce42068fa4179a8df856da", + "79c7c684e55b4338b9ce4673f7523835", + "0bfa55a53b104c658d50ca69670bb98e", + "bea1b71b56d240979aeb2f31339f39ab", + "295aff4e8e914f49928ae598dfa54b52", + "42d35f13fad1484ca00a0e4b41617f29" + ] + }, + "outputId": "ade588fd-1c58-4415-d9c9-6723b5e243d0" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/7.55k [00:00system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "0a596682a75b48aea7b9fca27354cba7", + "dbdaf11bba5f4ecb8053aef33be4ea5e", + "38a0cc67d05d497bafdaacf80182b7cf", + "1d2c9b5c146e4259ad6b169eb7da4c95", + "4ecadbf8928b449c95b52403d7ead0db", + "e3af50ac84eb40b59c0e7889867e65d8", + "63da8260e7d840799ddcbc7e2a413e13", + "bf1498a050db4489a7e4675839c69e9f", + "742eae1721bc4138996d1793df23ed91", + "b185bcf1ad794b25b58598b7bfb6a958", + "5e5a21a67bba4c4a84bad69171bf6ae5", + "ca4b19eefa2642fcaaacd37e9e5f8245", + "2d50e238e17945b3b41f19e7621437e3", + "52fca72c8e154095948b603bc86c67f5", + "634f19ab27ac4dceb0ef251bbee34d0a", + "e743585e4ee7455dbee07dd0d94f9823", + "8f5bd89c238249558527a5ab49a42063", + "d4665796761e4b33b9b6f5dddc784831", + "82ac76dfabd04cd3a98ac97b9151c242", + "6d849e4c66004f6a97e8000ec48d0c0e", + "d3187505ec0f4ceead04eb5722f9a2e8", + "3af641eb57eb4bf487000e2268396ee7", + "533410b2e86c4939b3037bcbc599d264", + "17db194fd6c846b181d2e774153317db", + "5cd8add8eeec4ece86e41c6456c2ed3b", + "22008837d9dd4ff7be55ed7b0562c9f3", + "b6438b1c95d14c4ead4c2a386b2d864a", + "3de1d193a653435887050b3eb84a3178", + "0f2598a432284228ac8d01c2c3152401", + "2e679c8bf9c34f2b81214e18944dccb6", + "e72c2daf5a1f41f1b2be0d3a7abf6942", + "8a82dd6ebf9e465db6a4381a340ac193", + "29ab9312d3b34395b49635cba322db87", + "498aba1432e142cbaacc0d4fd406c76b", + "11f98171eab04726a4a3c9aef80c2c29", + "01a2c2ba6a854b4ca908aabcd0c06799", + "83664322eb4047ee8dbaa396142d13ca", + "b469e37dde704c74bff8f09c5d31bea0", + "c2bb4df48fd448da9c12e6114704c26c", + "030b7ff93668430e9911a93451e8c2ef", + "7dc831efa549435ab73a8bfdd59330a2", + "b899ec71c79a47b8bc7ad07143fc238b", + "5b27a8a285344774adbfd1561817fc51", + "8e1426f9b75749c5b2dadccb76349fe5" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "a4285d66-750a-4871-cdb3-ea6a53a77329" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/1024 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
11.631300
21.851800
31.406800
41.483500
51.298500
60.871300
71.042400
80.976100
90.873000
100.906900
110.763200
120.825100
130.626800
140.521400
150.618600
160.615600
170.594100
180.475400
190.610600
200.420300
210.477600
220.735600
230.564100
240.234300
250.415800
260.430200
270.455600
280.638500
290.478200
300.673900
310.623100
320.542600
330.481000
340.430300
350.418000
360.544100
370.409500
380.551900
390.561300
400.305600
410.450900
420.591900
430.232700
440.538200
450.448300
460.265100
470.513700
480.613900
490.491800
500.464800
510.376400
520.508700
530.574700
540.402000
550.534500
560.453500
570.623500
580.446400
590.579200
600.321800

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pCqnaKmlO1U9", + "outputId": "fd1b6225-def1-4ef2-d7a7-2f01bfac4bbd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "259.5915 seconds used for training.\n", + "4.33 minutes used for training.\n", + "Peak reserved memory = 3.266 GB.\n", + "Peak reserved memory for training = 1.741 GB.\n", + "Peak reserved memory % of max memory = 22.156 %.\n", + "Peak reserved memory for training % of max memory = 11.811 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model!\n", + "\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "print(dataset[0][\"text\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lO9dA-_Wq9W9", + "outputId": "c651691c-c7f0-4672-8672-982f2ee577f7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|im_start|>system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Tool using inference util\n", + "import re\n", + "\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "def try_parse_tool_calls(content: str):\n", + " \"\"\"Try parse the tool calls.\"\"\"\n", + " tool_calls = []\n", + " offset = 0\n", + " for i, m in enumerate(re.finditer(r\"\\n(.+)?\\n\", content)):\n", + " if i == 0:\n", + " offset = m.start()\n", + " try:\n", + " func = json.loads(m.group(1))\n", + " tool_calls.append({\"type\": \"function\", \"function\": func})\n", + " if isinstance(func[\"arguments\"], str):\n", + " func[\"arguments\"] = json.loads(func[\"arguments\"])\n", + " except json.JSONDecodeError as e:\n", + " print(f\"Failed to parse tool calls: the content is {m.group(1)} and {e}\")\n", + " pass\n", + " if tool_calls:\n", + " if offset > 0 and content[:offset].strip():\n", + " c = content[:offset]\n", + " else:\n", + " c = \"\"\n", + " return {\"role\": \"assistant\", \"content\": c, \"tool_calls\": tool_calls}\n", + " return {\"role\": \"assistant\", \"content\": re.sub(r\"<\\|im_end\\|>$\", \"\", content)}\n" + ], + "metadata": { + "id": "AsF3E3RTes8w" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model determining which tool to call**\n", + "\n", + "We feed the model with a user message and a list of tools. It responds with a tool call in the following format:\n", + "```xml\n", + "Looking into my database... One sec.\n", + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "markdown", + "source": [ + "**In order** to use qwen-2.5's native tool calling with `transformers`, we must define our functions with Python and pass them as a parameter during `apply_chat_template`.\n", + "\n", + "**[NOTE]** to be correctly parsed a tool must have type annotations and a valid docstring." + ], + "metadata": { + "id": "sgZ4aF7bkq-L" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "4359a9a4-89e1-4819-9aec-2c4b892556c6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "#@title Tool Calling inference\n", + "def find_movie_details(title: str):\n", + " \"\"\"Find details about a movie based on its title\n", + " Args:\n", + " title: The title of the movie\n", + "\n", + " Returns:\n", + " dict: A dictionary containing the movie details\n", + " \"\"\"\n", + " if title == \"Inception\":\n", + " return {\"title\": \"Inception\", \"director\": \"Christopher Nolan\", \"release_year\": 2010, \"genre\": \"Science Fiction\", \"rating\": 8.8}\n", + " elif title == \"The Godfather\":\n", + " return {\"title\": \"The Godfather\", \"director\": \"Francis Ford Coppola\", \"release_year\": 1972, \"genre\": \"Crime, Drama\", \"rating\": 9.2}\n", + " else:\n", + " return {}\n", + "\n", + "\n", + "def play_music(genre: str, mood: str) -> None:\n", + " \"\"\"Play music based on user's preferences\n", + " Args:\n", + " genre: The genre of music to play\n", + " mood: The mood of the music to play\n", + " \"\"\"\n", + " pass\n", + "\n", + "\n", + "# easily accessible by name\n", + "function_name = {\n", + " \"find_movie_details\": find_movie_details,\n", + " \"play_music\": play_music,\n", + "}\n", + "\n", + "\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Good morning! What do you know about Inception? Please provide me all your info on this movie.\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response to the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "messages.append(try_parse_tool_calls(output_text))\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "if tool_calls := messages[-1].get(\"tool_calls\", None):\n", + " for tool_call in tool_calls:\n", + " if fn_call := tool_call.get(\"function\"):\n", + " fn_name: str = fn_call[\"name\"]\n", + " fn_args: dict = fn_call[\"arguments\"]\n", + "\n", + " fn_res: str = json.dumps(function_name[fn_name](**fn_args))\n", + "\n", + " messages.append({\n", + " \"role\": \"tool\",\n", + " \"name\": fn_name,\n", + " \"content\": fn_res,\n", + " })\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "\n", + "print(output_text)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3MzCKXBijw9j", + "outputId": "c8f76d88-e89a-4708-97eb-5d8b1bab9d8b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================\n", + "Inception is a science fiction film directed by Christopher Nolan that was released in 2010. It has an average rating of 8.8.<|im_end|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "a50cae76-90d6-4a69-b232-51910eef9343" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"relaxed\"}}\n", + "<|im_end|>\n", + "<|im_start|>system\n", + "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"find_movie_details\", \"description\": \"Find details about a movie based on its title\", \"parameters\": {\"type\": \"object\", \"properties\": {\"title\": {\"type\": \"string\", \"description\": \"The title of the movie\"}}, \"required\": [\"title\"]}}}\n", + "{\"type\": \"function\", \"function\": {\"name\": \"play_music\", \"description\": \"Play music based on user's preferences\", \"parameters\": {\"type\": \"object\", \"properties\": {\"genre\": {\"type\": \"string\", \"description\": \"The genre of music to play\"}, \"mood\": {\"type\": \"string\", \"description\": \"The mood of the music to play\"}}, \"required\": [\"genre\", \"mood\"]}, \"return\": {\"type\": \"null\"}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Please i want to listen some blues. play immediately<|im_end|>\n", + "<|im_start|>assistant\n", + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"happy\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues. play immediately\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "f8e7d3fe-8e4d-49ee-944f-08e70cdc1d87" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous tall tower in Paris?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "One of the most famous and iconic tall towers in Paris is the Eiffel Tower. Standing at 324 meters (1,063 feet) tall, this wrought iron tower is a symbol of the city and a must-see attraction for tourists from all over the world.<|end_of_text|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + "
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\n", + "\n", + "\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# Normally using pip install unsloth is enough\n", + "\n", + "# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n", + "# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n", + "!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n", + "!pip install --no-deps cut_cross_entropy unsloth_zoo\n", + "!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n", + "!pip install --no-deps unsloth" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QmUBVEnvCDJv", + "outputId": "ecdb1165-d4e1-4026-e535-030f14fe3917" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Llama patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + } + ], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "fourbit_models = [\n", + " \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 15 trillion tokens model 2x faster!\n", + " \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n", + " \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\", # We also uploaded 4bit for 405b!\n", + " \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n", + " \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n", + " \"unsloth/mistral-7b-v0.3-bnb-4bit\", # Mistral v3 2x faster!\n", + " \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n", + " \"unsloth/Phi-3.5-mini-instruct\", # Phi-3.5 2x faster!\n", + " \"unsloth/Phi-3-medium-4k-instruct\",\n", + " \"unsloth/gemma-2-9b-bnb-4bit\",\n", + " \"unsloth/gemma-2-27b-bnb-4bit\", # Gemma 2x faster!\n", + "] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SXd9bTZd1aaL" + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "6bZsfBuZDeCL", + "outputId": "113c510e-c08e-46f0-cfbc-1a2e0e16d470", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth 2025.2.15 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + ] + } + ], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `llama-3.1` we'll use the [user defined custom tools](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#user-defined-custom-tool-calling) template. If you want to use another model and/or template, you'll need to write your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Define system prompt and message delimiters\n", + "system_prompt = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "{functions}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{{\"example_name\": \"example_value\"}}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|>\"\"\"\n", + "\n", + "user_message = \"<|start_header_id|>user<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "assistant_tool_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eom_id|>\"\n", + "tool_response_message = \"<|start_header_id|>ipython<|end_header_id|>\\n\\n{}<|eot_id|>\"\n", + "assistant_continuation_prefix = \"<|start_header_id|>assistant<|end_header_id|> \"\n", + "assistant_continuation_message = \"<|start_header_id|>assistant<|end_header_id|> \\n\\n{}<|eot_id|>\"\n", + "function_string_template = \"\"\"Use the function '{name}' to: {description}\\n{schema}\"\"\"" + ], + "metadata": { + "id": "Vw8Ib-_zU9Eq", + "cellView": "form" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@title Util processing functions\n", + "import ast, json\n", + "\n", + "def convert_tool_format(tool):\n", + " func = tool.get(\"function\", {})\n", + " name = func.get(\"name\", \"\")\n", + " description = func.get(\"description\", \"\")\n", + " parameters_a = func.get(\"parameters\", {})\n", + " properties = parameters_a.get(\"properties\", {})\n", + " required_params = parameters_a.get(\"required\", [])\n", + " def map_type(a_type, a_format=None):\n", + " if a_type == \"string\":\n", + " return \"string\"\n", + " elif a_type == \"number\":\n", + " return \"int\"\n", + " elif a_type == \"boolean\":\n", + " return \"bool\"\n", + " return a_type\n", + " parameters_b = {}\n", + " for param, details in properties.items():\n", + " parameters_b[param] = {\n", + " \"param_type\": map_type(details.get(\"type\"), details.get(\"format\")),\n", + " \"description\": details.get(\"description\", \"\"),\n", + " \"required\": param in required_params\n", + " }\n", + " return {\n", + " \"name\": name,\n", + " \"description\": description,\n", + " \"parameters\": parameters_b\n", + " }\n", + "\n", + "def get_function_string(f):\n", + " converted_tool = convert_tool_format(f)\n", + " return function_string_template.format(\n", + " name=converted_tool[\"name\"],\n", + " description=converted_tool[\"description\"],\n", + " schema=json.dumps(converted_tool)\n", + " )\n", + "\n", + "def convert_function_call_format(call):\n", + " func_data = call.get(\"function\", {})\n", + " func_name = func_data.get(\"name\", \"\")\n", + " arguments_str = func_data.get(\"arguments\", \"{}\")\n", + " try:\n", + " arguments_dict = ast.literal_eval(arguments_str)\n", + " except Exception:\n", + " arguments_dict = {}\n", + " arguments_json = json.dumps(arguments_dict)\n", + " return f\"{arguments_json}\"\n", + "\n", + "def process_block(block):\n", + " tool_index = None\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and \"tool_calls\" in msg:\n", + " tool_index = i\n", + " break\n", + " filtered_block = []\n", + " if tool_index is not None:\n", + " for i, msg in enumerate(block):\n", + " if msg[\"role\"] == \"assistant\" and i < tool_index:\n", + " continue\n", + " filtered_block.append(msg)\n", + " else:\n", + " filtered_block = block\n", + " block_context = \"\"\n", + " tool_called = False\n", + " for msg in filtered_block:\n", + " if msg[\"role\"] == \"assistant\":\n", + " if \"tool_calls\" in msg:\n", + " block_context += assistant_tool_message.format(convert_function_call_format(msg[\"tool_calls\"][0]))\n", + " else:\n", + " if tool_called:\n", + " block_context += assistant_continuation_message.format(msg[\"content\"])\n", + " tool_called = False\n", + " else:\n", + " block_context += assistant_message.format(msg[\"content\"])\n", + " elif msg[\"role\"] == \"tool\":\n", + " block_context += tool_response_message.format(msg[\"content\"])\n", + " tool_called = True\n", + " return block_context\n", + "\n", + "def get_formatted_sample(sample):\n", + " functions_string = \"\\n\\n\".join([get_function_string(f) for f in sample.get(\"tools\", [])])\n", + " context = system_prompt.format(functions=functions_string)\n", + " block = []\n", + " for message in sample[\"messages\"]:\n", + " if message[\"role\"] == \"system\":\n", + " continue\n", + " elif message[\"role\"] == \"user\":\n", + " if block:\n", + " context += process_block(block)\n", + " block = []\n", + " context += user_message.format(message[\"content\"])\n", + " else:\n", + " block.append(message)\n", + " if block:\n", + " context += process_block(block)\n", + " return context\n" + ], + "metadata": { + "id": "8vdlWCEoVAN2" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "a260c69c1bc24faaba45c4d96f6ea2f6", + "3ed1d73212784ebf81c2496cff7e1f2a", + "53565e6b78004ef9b447121f84985bb7", + "86edadf55c99416e911ad55d52494038", + "819ba37f02e549b3bd4b3b7b87f56d1f", + "ea72b5a4fcc54096870d16e6ecb0ca3a", + "7f7aeb38c44a4732af38180fcfb1da6c", + "c607cb289d05404386be3c5c87d83836", + "a5f08b69b7e4432e9d983ac974e09b38", + "9b727b7e5455450d94724eec947e3004", + "848e8392e481410badcdccd57d13dee7" + ] + }, + "outputId": "0555f238-be41-4bbf-b0d6-3e94ea231cc9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/112754 [00:00<|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'track_calories' to: Track daily calorie intake\n", + "{\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"meal\": {\"param_type\": \"string\", \"description\": \"The meal for which calories are being tracked\", \"required\": true}, \"calories\": {\"param_type\": \"int\", \"description\": \"The number of calories consumed\", \"required\": true}, \"date\": {\"param_type\": \"string\", \"description\": \"The date for which calories are being tracked\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}<|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "3237e04b476b4142ae8a0309dcdf327a", + "8b94291cab6240c596621923a4bfc213", + "6c3fb60f196a46799685f7cbbb0be28e", + "c259833aa6d148749293cb0bd7849089", + "a32b643a370d4c8faac31cb30aba28dd", + "25cba0c7c3b64666879ea3157d4b43fb", + "e5cf8fa8df7e458881e2fc468cc707e4", + "fe67b32b5edd4808916aa9e63a8c8d9b", + "7b5ba5b5da384cbcb422f945740a755d", + "808f9973de6844b584dd53b630a398e5", + "e1a68f9deb114c0e8da8229edf323926", + "b5b51a206563427eac85fe849439b99f", + "ba61fdc809444053969d58813a608d6d", + "cc40514e343f4f559049b616b2eacc1f", + "7db6511d4abe4e358ad04ae5ec166140", + "e0785da126f547748282d18c4b79fd58", + "19343326b2de49079565b8e64b67e031", + "458d4fb43fd34a54bf4c6afb8dff2849", + "74a06f38b6f54ce6a21ce43edc9f1641", + "2e5ea29454bb462a9d2eaef3aaf4b186", + "be011cc34c674ef9a237858b6ff8f728", + "f3f34dd0896847fc9161d1a158f60f13", + "3b063da052a44e7abfb00a1f52945e2a", + "1e73832bd5ad4d808a719e446c78dec0", + "5e25580d97b34fafa75f9b1dc3d1882f", + "6e7c8c1e2c434f468eb2f412031d4f7c", + "b9b6dc43ef474d3a972f2e4ee6852dca", + "89d0ea94a2314c7a8a88943cf641a649", + "04e79c7f06294ab8a8074e2e2916fa56", + "2f17a530623049b59b1a5197e7f80370", + "40915aa8e4e84dd7a58c0738e707fe1b", + "3efc83359f7b4d99b70d6ab9fa25d23a", + "91fa89bb400741b4a0fe046b1365dc42", + "6cd1b567f37e479eb3d1cdab522dd39d", + "4997c8e41a3e423a9b3ebe0081746931", + "01825c2a354d40799555a990ba73996b", + "8f2fd22e31f4485ba97f2fe38e61c9f0", + "1921af3669174ac087763cdc506ee76d", + "269f717369c64b90b7e80fbb9c1a9997", + "4fb7436f92ee48cdac83c344904dcccf", + "eb7a01cb8f3848eb94734efa0aee1e61", + "4dc4406ad32e4abf8a491fb59006b491", + "87334ad1ff3f4281977968469e37183d", + "ea5cc23294cc474e93840332d604971a" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "3bfafb60-d7f4-45b5-9937-4b3d1d84bc14" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/112754 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
12.022000
22.147200
32.373700
41.681200
51.539600
61.761500
71.689500
81.524600
91.107300
101.122800
110.948900
120.939600
130.820500
140.788200
150.609800
160.652800
170.639200
180.374600
190.437900
200.420400
210.449600
220.460100
230.490300
240.400400
250.287900
260.439200
270.502500
280.201300
290.442800
300.377000
310.598200
320.460600
330.201500
340.437300
350.341100
360.322100
370.595400
380.289700
390.415600
400.298400
410.492500
420.508200
430.188600
440.425400
450.261800
460.506300
470.411200
480.165600
490.544000
500.265200
510.476600
520.308800
530.237200
540.361200
550.309200
560.375700
570.369600
580.333500
590.573200
600.230900

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "cellView": "form", + "id": "pCqnaKmlO1U9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67e4f04c-5ade-4097-b775-5d6b27a6289a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1263.8918 seconds used for training.\n", + "21.06 minutes used for training.\n", + "Peak reserved memory = 7.467 GB.\n", + "Peak reserved memory for training = 1.951 GB.\n", + "Peak reserved memory % of max memory = 50.655 %.\n", + "Peak reserved memory for training % of max memory = 13.235 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! We'll load the `test` split of our dataset and prepare it to generation.\n", + "\n", + "**[NOTE]** To use the model's tool calling capabilities in a more streamlined way you should use a scaffolding framework such as [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps). For the scope of this demo we will test the model manually.\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "dataset_test = load_dataset(\"madroid/glaive-function-calling-openai\", split = \"test\")\n", + "dataset_test = dataset_test.map(formatting_prompts_func, batched = True,)" + ], + "metadata": { + "id": "5iUqU8oqg1Ij", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "3623ca82f6ef49c285ffde06c2265d47", + "f12f63657f344aaaae37d0b926ef2a67", + "174598c6bb8946bfb288980ecfb41043", + "e57a6673a2b24f61a86952868d19398b", + "03aa9cc5cce4488dbf4d82d62e72af16", + "5c29f4e94b2647f3abadfacaa590e2bb", + "70701b3922a84f0eaaa8eaffeb9787d3", + "2c2099110ef84a89a8eac9b4ac02106a", + "5c38cfd5e75f4ab98d1613f95fcf8733", + "52a792e911f649b9b051ce1578832686", + "2b09ff9587ab4ff5a07a8e46bb49b413" + ] + }, + "outputId": "0a0ab140-af55-4dbc-e2c1-b85e44e2eae9" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/967 [00:00{\"example_name\": \"example_value\"}<|eom_id|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "d87f4648-2361-472f-9bab-3618bc8ed6ca" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "test_sample = dataset_test[128][\"text\"]\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title **User-defined Custom tools**\n", + "import re\n", + "import json\n", + "\n", + "\n", + "# function to parse model's response\n", + "def parse_function_call(s: str):\n", + " # Regex pattern to extract function name and JSON arguments\n", + " match = re.search(r\"(\\{.*?\\})\", s)\n", + "\n", + " if match:\n", + " function_name = match.group(1) # Extract function name\n", + " args_json = match.group(2) # Extract JSON string\n", + " args = json.loads(args_json) # Parse JSON to dictionary\n", + " return function_name, args\n", + " else:\n", + " return None, None\n", + "\n", + "\n", + "# CUSTOM TOOLS\n", + "def calculate_loan_emi(loan_amount: int, interest_rate: int, loan_term: int) -> float:\n", + " monthly_interest_rate = (interest_rate / 100) / 12\n", + "\n", + " if monthly_interest_rate == 0:\n", + " emi = loan_amount / loan_term\n", + " else:\n", + " emi = (loan_amount * monthly_interest_rate * (1 + monthly_interest_rate) ** loan_term) / \\\n", + " ((1 + monthly_interest_rate) ** loan_term - 1)\n", + "\n", + " return round(emi, 2)\n", + "\n", + "\n", + "def calculate_fuel_consumption(distance: int, fuel_efficiency: int) -> float:\n", + " if fuel_efficiency <= 0:\n", + " raise ValueError(\"Fuel efficiency must be greater than zero.\")\n", + "\n", + " fuel_consumed = distance / fuel_efficiency\n", + " return round(fuel_consumed, 2)\n", + "\n", + "\n", + "TOOLS = {\n", + " \"calculate_loan_emi\": calculate_loan_emi,\n", + " \"calculate_fuel_consumption\": calculate_fuel_consumption,\n", + "}" + ], + "metadata": { + "id": "ln4cwSVJqLjh" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "# Parse and execute tool given model output\n", + "function_name, arguments = parse_function_call(output_text)\n", + "\n", + "\n", + "if function_name is not None:\n", + " tool_response = TOOLS[function_name](**arguments)\n", + "\n", + " # Prepare context\n", + " context = test_sample # original input\n", + " # Add tool call and response\n", + " context += assistant_tool_message.format(output_text)\n", + " context += tool_response_message.format(tool_response)\n", + " # Add generation prompt\n", + " context += assistant_continuation_prefix\n", + "\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + " inputs = tokenizer(\n", + " [\n", + " context\n", + " ], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + " print(context)\n", + " print(\"===\"*20)\n", + "\n", + " outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + " output_text_chat = tokenizer.batch_decode(outputs)\n", + " output_text_chat = output_text_chat[0][len(context):]\n", + " print(output_text_chat)" + ], + "metadata": { + "id": "Ww_lGt0_uj82", + "outputId": "0b8d80ea-d017-493f-9509-e62804d8e71d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|><|eom_id|><|start_header_id|>ipython<|end_header_id|>\n", + "\n", + "25.0<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "============================================================\n", + " \n", + "\n", + "The fuel consumption for your car would be 25.0 liters.<|eot_id|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "6a7c4e8a-3015-4026-d5f2-bf131c5b4953" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " test_sample+assistant_continuation_prefix,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "973e4561-f354-4595-eaf9-1a0f7f18fc4f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "4297ecc7-fac6-4c8e-8e50-6f5887fc9ed0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "\n", + "Environment: ipython\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 23 July 2024\n", + "\n", + "# Tool Instructions\n", + "- Always execute python code in messages that you share.\n", + "- When looking for real time information use relevant functions if available else let the user know\n", + "\n", + "\n", + "\n", + "You have access to the following functions:\n", + "\n", + "Use the function 'calculate_fuel_consumption' to: Calculate the fuel consumption based on distance and fuel efficiency\n", + "{\"name\": \"calculate_fuel_consumption\", \"description\": \"Calculate the fuel consumption based on distance and fuel efficiency\", \"parameters\": {\"distance\": {\"param_type\": \"int\", \"description\": \"The distance traveled\", \"required\": true}, \"fuel_efficiency\": {\"param_type\": \"int\", \"description\": \"The fuel efficiency in kilometers per liter\", \"required\": true}}}\n", + "\n", + "\n", + "If a you choose to call a function ONLY reply in the following format:\n", + "<{start_tag}={function_name}>{parameters}{end_tag}\n", + "where\n", + "\n", + "start_tag => ` a JSON dict with the function argument name as key and function argument value as value.\n", + "end_tag => ``\n", + "\n", + "Here is an example,\n", + "{\"example_name\": \"example_value\"}\n", + "\n", + "Reminder:\n", + "- Function calls MUST follow the specified format\n", + "- Required parameters MUST be specified\n", + "- Only call one function at a time\n", + "- Put the entire function call reply on one line\n", + "- Always add your sources when using search results to answer the user query\n", + "\n", + "You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "Hi, I need to calculate the fuel consumption for my car. I have traveled 500 kilometers and my car's fuel efficiency is 20 kilometers per liter. Can you help me with that?<|eot_id|><|start_header_id|>assistant<|end_header_id|> \n", + "==============================\n", + " \n", + "\n", + "{\"distance\": 500, \"fuel_efficiency\": 20}<|eom_id|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "test_sample = dataset_test[128][\"text\"]\n", + "context = test_sample+assistant_continuation_prefix\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "output_text = tokenizer.batch_decode(outputs)[0]\n", + "\n", + "\n", + "output_text = output_text[len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
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a/original_template/Qwen2.5_(1.5B)-Tool_Calling.ipynb b/original_template/Qwen2.5_(1.5B)-Tool_Calling.ipynb new file mode 100644 index 00000000..019dc693 --- /dev/null +++ b/original_template/Qwen2.5_(1.5B)-Tool_Calling.ipynb @@ -0,0 +1,8075 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Gpaavfkxjn4I" + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EVvdJhktjn4N" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XH64024jn4O" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wcPI_Fhrjn4O" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZmVkatYxjn4P" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# Normally using pip install unsloth is enough\n", + "\n", + "# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n", + "# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n", + "!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n", + "!pip install --no-deps cut_cross_entropy unsloth_zoo\n", + "!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n", + "!pip install --no-deps unsloth" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQZ7n7kGjn4Q" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412, + "referenced_widgets": [ + "71f9cb34387047e0841f3d7143b09eff", + "f77a064e66d5494e8dfee6c9778d55ae", + 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Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/1.53G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Glaive Function Calling dataset from [madroid](https://huggingface.co/datasets/madroid/glaive-function-calling-openai), which is a version of the original [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) pre-processed to facilitate integration. You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** Each model has its own Tool Calling template. For `qwen-2.5` we'll use the [official template](https://qwen.readthedocs.io/en/latest/framework/function_call.html#hugging-face-transformers). If you want to use another model and/or template, you'll need to write your own data prep. See [this notebook](https://colab.research.google.com/drive/1-1FbzLnx1DWRa8ysx5KUlhvRtaToCbvV?usp=sharing) for a demo with `llama-3.1-8B`.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Process dataset util\n", + "import json\n", + "import random\n", + "from datasets import load_dataset\n", + "import ast\n", + "\n", + "\n", + "# This is our transformation function that creates the new chat format.\n", + "def get_formatted_sample(item):\n", + " # Parse the JSON string from the dataset sample\n", + " sample = item\n", + " tools = sample.get(\"tools\", [])\n", + "\n", + " # new_history will store the final sequence of messages.\n", + " new_history = []\n", + " # pending_assistant is used to merge consecutive assistant messages.\n", + " pending_assistant = None\n", + " # Mapping from tool call id to function name so we can later label the tool response.\n", + " mapping_tool_id_to_function_name = {}\n", + "\n", + " # Process each message in the original sample.\n", + " for msg in sample[\"messages\"]:\n", + " role = msg.get(\"role\")\n", + "\n", + " if role == \"system\":\n", + " # Flush any pending assistant message\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " # Append system message as-is.\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"user\":\n", + " # Flush any pending assistant message before adding a new user message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " elif role == \"assistant\":\n", + " # If we haven't started merging an assistant message yet, start one.\n", + " if pending_assistant is None:\n", + " pending_assistant = {\"role\": \"assistant\", \"content\": \"\", \"tool_calls\": []}\n", + "\n", + " # Merge textual content if present.\n", + " if \"content\" in msg and msg[\"content\"]:\n", + " if pending_assistant[\"content\"]:\n", + " # Append on a new line if already exists.\n", + " pending_assistant[\"content\"] += \"\\n\" + msg[\"content\"]\n", + " else:\n", + " pending_assistant[\"content\"] = msg[\"content\"]\n", + "\n", + " # Process any tool_calls: remove the \"id\" field and record a mapping.\n", + " if \"tool_calls\" in msg:\n", + " for tc in msg[\"tool_calls\"]:\n", + " # Map the id to function name if present.\n", + " if \"id\" in tc:\n", + " mapping_tool_id_to_function_name[tc[\"id\"]] = tc[\"function\"][\"name\"]\n", + " function_name = tc[\"function\"][\"name\"]\n", + " arguments = tc[\"function\"][\"arguments\"]\n", + " if isinstance(arguments, str):\n", + " arguments = ast.literal_eval(arguments)\n", + " pending_assistant[\"tool_calls\"].append({\n", + " \"name\": function_name,\n", + " \"arguments\": arguments\n", + " })\n", + "\n", + " elif role == \"tool\":\n", + " # For tool responses, we expect a tool_call_id that maps back to a tool call.\n", + " tool_call_id = msg.get(\"tool_call_id\")\n", + " function_name = mapping_tool_id_to_function_name.get(tool_call_id, \"\")\n", + " # Create a tool response message in the chat format (role 'user' with the \"name\" set to the function name).\n", + " tool_response = {\n", + " \"role\": \"user\",\n", + " \"name\": function_name,\n", + " \"content\": msg.get(\"content\", \"\")\n", + " }\n", + " # Flush any pending assistant message before appending the tool response.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(tool_response)\n", + "\n", + " else:\n", + " # For any unknown roles, flush and then append as-is.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + " pending_assistant = None\n", + " new_history.append(msg)\n", + "\n", + " # Flush any remaining pending assistant message.\n", + " if pending_assistant is not None:\n", + " new_history.append(pending_assistant)\n", + "\n", + " # Now apply the chat template to the reconstructed history.\n", + " context = tokenizer.apply_chat_template(\n", + " new_history,\n", + " tools=tools,\n", + " tokenize=False,\n", + " add_generation_prompt=False,\n", + " )\n", + " return context" + ], + "metadata": { + "id": "8vdlWCEoVAN2", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "4320dca140a04d61be7fcc6b6c365cfd", + "6d16b778adc34ac0bc7954e277971111", + "3eef8d56318b423fb0584a51cb3467f3", + "fe2c33ec611149ec8856ad05a662e65b", + "bd02734c6e31420ebbb4eec37224bb2a", + "c5ac0299f44c4e2c8a2f7bf9da7d6028", + "cb62b2e68a2c44909c042f2591e94fe4", + "ac0ae1380d7d4e58bed5c586a283cef2", + "5be3ad2ca9da45d8944efee9ee143a7e", + "68b5768d28f04c8297e72645135573af", + "10d58a9944cd46aca717b011262a1009", + "6be075556fa5432595199d632142a8fc", + "7ce5b60e4026463ea23effefeca77e68", + "d0b13f82c73142baa769533a8ffc77ab", + "bb84e9ffcafa4204adf4758afc4d0927", + "c1517a0f7df9480e9f478fc889d1eb0f", + "2d73fbc2e8b64721bbd933df0447a6d3", + "7f6a66d72af5404c907d092ceb5659aa", + "1e3da677909f4ec6b38d177a14f74760", + "008a1349c17e4578b41c44ab27f516e0", + "268240e741d04efaaad59952e6af7ae0", + "b21e3063ff704f9683aadf3cfe43b8c5", + "0220656634664a6f833745dde572541a", + "337d6d214a794128a85ca5060c22bb1b", + "1879f7f262504696901619247f577d14", + "4ffcedaba2244a21ab6bd0d4afc99402", + "3c0bc15cf4184ab1b1dc4428e182e40c", + "9bc456a3719e4791b962b05bc6981476", + "9a38cd248e684fa0b0ba0f5cddaf6b8d", + "4b4a215c60114d71b10f71661414cc3f", + "93181ef56e4e4ef9be43c3d3c6f5430d", + "23a81673196e454b9676ea068f813208", + "8b052675f07349a48a52642c604dde2c", + "5c8683503d2d48af8d72874bef8eaf56", + "5f3ca5f33c4b4835a61df6b25724dfe8", + "bb479926e93d48a2af69aaf9885fb84b", + "68fae34fe47146f686aa5b50a4c24da9", + "79a6b359fdca402daf7c02b4db8e5d6f", + "0c1d30dabd4a4868986cdd31646cf2bb", + "7506ccd49a22485594ec4618a899a396", + "c16b0c0ab1a24d18961939898e048de8", + "44da95bb6f734abfaa8d2a943b627643", + "a290dad18a3f4ecc89738f5b634b341d", + "b90cf08b01b84ec7aa06f66b3415bc89", + "f406ea625b374112b946e88bfbaaa6a6", + "4178edaf1af548a7882e9beb2521ecbb", + "ab4fc29e01b842ceb0223e92165d61ae", + "bdedd3b48b0048719da51cd985c2cd62", + "b323d18298e542d0be87ca336b3e5943", + "a426acfcdd524858a54e8fce2e5c62c7", + "3fc6247935404d07802df9ee928834d8", + "b6878b2b2a8f4f258b89c44ed2370221", + "c66e238c7ef943febef3a9cde3f504a0", + "e5e95f737d8c4e7db6da6222e157deab", + "9d2c942db1c746eda133f1ab13392115", + "867f653d38f64c0f972a62bbbcb65b48", + "9155ada59e6440358cea72942b3eb292", + "870767b871554d1b8b3e27e798a63cea", + "d5c479f39fce4bd48a3b2d5121b8af1b", + "a3b93cd9c53f46fca79e592ac8d8dca4", + "165c8fc782ce42068fa4179a8df856da", + "79c7c684e55b4338b9ce4673f7523835", + "0bfa55a53b104c658d50ca69670bb98e", + "bea1b71b56d240979aeb2f31339f39ab", + "295aff4e8e914f49928ae598dfa54b52", + "42d35f13fad1484ca00a0e4b41617f29" + ] + }, + "outputId": "ade588fd-1c58-4415-d9c9-6723b5e243d0" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/7.55k [00:00system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "0a596682a75b48aea7b9fca27354cba7", + "dbdaf11bba5f4ecb8053aef33be4ea5e", + "38a0cc67d05d497bafdaacf80182b7cf", + "1d2c9b5c146e4259ad6b169eb7da4c95", + "4ecadbf8928b449c95b52403d7ead0db", + "e3af50ac84eb40b59c0e7889867e65d8", + "63da8260e7d840799ddcbc7e2a413e13", + "bf1498a050db4489a7e4675839c69e9f", + "742eae1721bc4138996d1793df23ed91", + "b185bcf1ad794b25b58598b7bfb6a958", + "5e5a21a67bba4c4a84bad69171bf6ae5", + "ca4b19eefa2642fcaaacd37e9e5f8245", + "2d50e238e17945b3b41f19e7621437e3", + "52fca72c8e154095948b603bc86c67f5", + "634f19ab27ac4dceb0ef251bbee34d0a", + "e743585e4ee7455dbee07dd0d94f9823", + "8f5bd89c238249558527a5ab49a42063", + "d4665796761e4b33b9b6f5dddc784831", + "82ac76dfabd04cd3a98ac97b9151c242", + "6d849e4c66004f6a97e8000ec48d0c0e", + "d3187505ec0f4ceead04eb5722f9a2e8", + "3af641eb57eb4bf487000e2268396ee7", + "533410b2e86c4939b3037bcbc599d264", + "17db194fd6c846b181d2e774153317db", + "5cd8add8eeec4ece86e41c6456c2ed3b", + "22008837d9dd4ff7be55ed7b0562c9f3", + "b6438b1c95d14c4ead4c2a386b2d864a", + "3de1d193a653435887050b3eb84a3178", + "0f2598a432284228ac8d01c2c3152401", + "2e679c8bf9c34f2b81214e18944dccb6", + "e72c2daf5a1f41f1b2be0d3a7abf6942", + "8a82dd6ebf9e465db6a4381a340ac193", + "29ab9312d3b34395b49635cba322db87", + "498aba1432e142cbaacc0d4fd406c76b", + "11f98171eab04726a4a3c9aef80c2c29", + "01a2c2ba6a854b4ca908aabcd0c06799", + "83664322eb4047ee8dbaa396142d13ca", + "b469e37dde704c74bff8f09c5d31bea0", + "c2bb4df48fd448da9c12e6114704c26c", + "030b7ff93668430e9911a93451e8c2ef", + "7dc831efa549435ab73a8bfdd59330a2", + "b899ec71c79a47b8bc7ad07143fc238b", + "5b27a8a285344774adbfd1561817fc51", + "8e1426f9b75749c5b2dadccb76349fe5" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "a4285d66-750a-4871-cdb3-ea6a53a77329" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/1024 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
11.631300
21.851800
31.406800
41.483500
51.298500
60.871300
71.042400
80.976100
90.873000
100.906900
110.763200
120.825100
130.626800
140.521400
150.618600
160.615600
170.594100
180.475400
190.610600
200.420300
210.477600
220.735600
230.564100
240.234300
250.415800
260.430200
270.455600
280.638500
290.478200
300.673900
310.623100
320.542600
330.481000
340.430300
350.418000
360.544100
370.409500
380.551900
390.561300
400.305600
410.450900
420.591900
430.232700
440.538200
450.448300
460.265100
470.513700
480.613900
490.491800
500.464800
510.376400
520.508700
530.574700
540.402000
550.534500
560.453500
570.623500
580.446400
590.579200
600.321800

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pCqnaKmlO1U9", + "outputId": "fd1b6225-def1-4ef2-d7a7-2f01bfac4bbd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "259.5915 seconds used for training.\n", + "4.33 minutes used for training.\n", + "Peak reserved memory = 3.266 GB.\n", + "Peak reserved memory for training = 1.741 GB.\n", + "Peak reserved memory % of max memory = 22.156 %.\n", + "Peak reserved memory for training % of max memory = 11.811 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ekOmTR1hSNcr" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model!\n", + "\n", + "\n", + "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" + ] + }, + { + "cell_type": "code", + "source": [ + "print(dataset[0][\"text\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lO9dA-_Wq9W9", + "outputId": "c651691c-c7f0-4672-8672-982f2ee577f7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|im_start|>system\n", + "You are a helpful assistant with access to the following functions. Use them if required\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"track_calories\", \"description\": \"Track daily calorie intake\", \"parameters\": {\"type\": \"object\", \"properties\": {\"meal\": {\"type\": \"string\", \"description\": \"The meal for which calories are being tracked\"}, \"calories\": {\"type\": \"number\", \"description\": \"The number of calories consumed\"}, \"date\": {\"type\": \"string\", \"format\": \"date\", \"description\": \"The date for which calories are being tracked\"}}, \"required\": [\"meal\", \"calories\", \"date\"]}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hi, I had a pizza for lunch today which was about 800 calories. Can you track this for me?<|im_end|>\n", + "<|im_start|>assistant\n", + "Sure, I can help you with that. Let me track this for you.\n", + "\n", + "{\"name\": \"track_calories\", \"arguments\": {\"meal\": \"pizza\", \"calories\": 800, \"date\": \"2022-03-01\"}}\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "{\"status\": \"success\", \"message\": \"Calories for your pizza meal have been successfully tracked for the date 2022-03-01\"}<|im_end|>\n", + "<|im_start|>assistant\n", + "Great! The calories for your pizza meal have been successfully tracked for today.<|im_end|>\n", + "<|im_start|>user\n", + "That's awesome! Can you also order a pizza for me from the nearest pizza place?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as placing orders. My primary function is to assist you with the functions provided to me, such as tracking your calorie intake.<|im_end|>\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Tool using inference util\n", + "import re\n", + "\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "def try_parse_tool_calls(content: str):\n", + " \"\"\"Try parse the tool calls.\"\"\"\n", + " tool_calls = []\n", + " offset = 0\n", + " for i, m in enumerate(re.finditer(r\"\\n(.+)?\\n\", content)):\n", + " if i == 0:\n", + " offset = m.start()\n", + " try:\n", + " func = json.loads(m.group(1))\n", + " tool_calls.append({\"type\": \"function\", \"function\": func})\n", + " if isinstance(func[\"arguments\"], str):\n", + " func[\"arguments\"] = json.loads(func[\"arguments\"])\n", + " except json.JSONDecodeError as e:\n", + " print(f\"Failed to parse tool calls: the content is {m.group(1)} and {e}\")\n", + " pass\n", + " if tool_calls:\n", + " if offset > 0 and content[:offset].strip():\n", + " c = content[:offset]\n", + " else:\n", + " c = \"\"\n", + " return {\"role\": \"assistant\", \"content\": c, \"tool_calls\": tool_calls}\n", + " return {\"role\": \"assistant\", \"content\": re.sub(r\"<\\|im_end\\|>$\", \"\", content)}\n" + ], + "metadata": { + "id": "AsF3E3RTes8w" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model determining which tool to call**\n", + "\n", + "We feed the model with a user message and a list of tools. It responds with a tool call in the following format:\n", + "```xml\n", + "Looking into my database... One sec.\n", + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n", + "```" + ], + "metadata": { + "id": "ke46c6SptW9m" + } + }, + { + "cell_type": "markdown", + "source": [ + "**In order** to use qwen-2.5's native tool calling with `transformers`, we must define our functions with Python and pass them as a parameter during `apply_chat_template`.\n", + "\n", + "**[NOTE]** to be correctly parsed a tool must have type annotations and a valid docstring." + ], + "metadata": { + "id": "sgZ4aF7bkq-L" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "4359a9a4-89e1-4819-9aec-2c4b892556c6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"find_movie_details\", \"arguments\": {\"title\": \"Inception\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "#@title Tool Calling inference\n", + "def find_movie_details(title: str):\n", + " \"\"\"Find details about a movie based on its title\n", + " Args:\n", + " title: The title of the movie\n", + "\n", + " Returns:\n", + " dict: A dictionary containing the movie details\n", + " \"\"\"\n", + " if title == \"Inception\":\n", + " return {\"title\": \"Inception\", \"director\": \"Christopher Nolan\", \"release_year\": 2010, \"genre\": \"Science Fiction\", \"rating\": 8.8}\n", + " elif title == \"The Godfather\":\n", + " return {\"title\": \"The Godfather\", \"director\": \"Francis Ford Coppola\", \"release_year\": 1972, \"genre\": \"Crime, Drama\", \"rating\": 9.2}\n", + " else:\n", + " return {}\n", + "\n", + "\n", + "def play_music(genre: str, mood: str) -> None:\n", + " \"\"\"Play music based on user's preferences\n", + " Args:\n", + " genre: The genre of music to play\n", + " mood: The mood of the music to play\n", + " \"\"\"\n", + " pass\n", + "\n", + "\n", + "# easily accessible by name\n", + "function_name = {\n", + " \"find_movie_details\": find_movie_details,\n", + " \"play_music\": play_music,\n", + "}\n", + "\n", + "\n", + "\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Good morning! What do you know about Inception? Please provide me all your info on this movie.\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Result from calling the tool is passed back to the model and it generates the final response to the user**\n", + "\n", + "Now we add the tool call from the previous generation and its result to the context, the model then generates the final response." + ], + "metadata": { + "id": "sFiDaiRPuOzw" + } + }, + { + "cell_type": "code", + "source": [ + "messages.append(try_parse_tool_calls(output_text))\n", + "\n", + "# https://qwen.readthedocs.io/en/latest/framework/function_call.html#id3\n", + "if tool_calls := messages[-1].get(\"tool_calls\", None):\n", + " for tool_call in tool_calls:\n", + " if fn_call := tool_call.get(\"function\"):\n", + " fn_name: str = fn_call[\"name\"]\n", + " fn_args: dict = fn_call[\"arguments\"]\n", + "\n", + " fn_res: str = json.dumps(function_name[fn_name](**fn_args))\n", + "\n", + " messages.append({\n", + " \"role\": \"tool\",\n", + " \"name\": fn_name,\n", + " \"content\": fn_res,\n", + " })\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "\n", + "print(context)\n", + "print(\"===\"*10)\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "\n", + "print(output_text)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3MzCKXBijw9j", + "outputId": "c8f76d88-e89a-4708-97eb-5d8b1bab9d8b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================\n", + "Inception is a science fiction film directed by Christopher Nolan that was released in 2010. It has an average rating of 8.8.<|im_end|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "a50cae76-90d6-4a69-b232-51910eef9343" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"relaxed\"}}\n", + "<|im_end|>\n", + "<|im_start|>system\n", + "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\n", + "\n", + "# Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "{\"type\": \"function\", \"function\": {\"name\": \"find_movie_details\", \"description\": \"Find details about a movie based on its title\", \"parameters\": {\"type\": \"object\", \"properties\": {\"title\": {\"type\": \"string\", \"description\": \"The title of the movie\"}}, \"required\": [\"title\"]}}}\n", + "{\"type\": \"function\", \"function\": {\"name\": \"play_music\", \"description\": \"Play music based on user's preferences\", \"parameters\": {\"type\": \"object\", \"properties\": {\"genre\": {\"type\": \"string\", \"description\": \"The genre of music to play\"}, \"mood\": {\"type\": \"string\", \"description\": \"The mood of the music to play\"}}, \"required\": [\"genre\", \"mood\"]}, \"return\": {\"type\": \"null\"}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Please i want to listen some blues. play immediately<|im_end|>\n", + "<|im_start|>assistant\n", + "\n", + "{\"name\": \"play_music\", \"arguments\": {\"genre\": \"blues\", \"mood\": \"happy\"}}\n", + "<|im_end|>\n" + ] + } + ], + "source": [ + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues. play immediately\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "f8e7d3fe-8e4d-49ee-944f-08e70cdc1d87" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous tall tower in Paris?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "One of the most famous and iconic tall towers in Paris is the Eiffel Tower. Standing at 324 meters (1,063 feet) tall, this wrought iron tower is a symbol of the city and a must-see attraction for tourists from all over the world.<|end_of_text|>\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"Please i want to listen some blues\"},\n", + "]\n", + "\n", + "\n", + "context = tokenizer.apply_chat_template(\n", + " messages,\n", + " tools=[find_movie_details, play_music],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "inputs = tokenizer(\n", + "[\n", + " context,\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "\n", + "output_text = tokenizer.batch_decode(outputs)[0][len(context):]\n", + "print(output_text)\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", + "\n", + "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lh6A70Xzjn4Z" + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
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