diff --git "a/notebooks/bonus-unit1.ipynb" "b/notebooks/bonus-unit1.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/bonus-unit1.ipynb" @@ -0,0 +1,6378 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "43b502c1-9548-4580-84ad-1cbac158edb8", + "metadata": { + "id": "43b502c1-9548-4580-84ad-1cbac158edb8" + }, + "source": [ + "# Bonus Unit 1: Fine-Tuning a model for Function-Calling\n", + "\n", + "In this tutorial, **we're going to Fine-Tune an LLM for Function Calling.**\n", + "\n", + "This notebook is part of the Hugging Face Agents Course, a free Course from beginner to expert, where you learn to build Agents.\n", + "\n", + "\"Agent\n" + ] + }, + { + "cell_type": "markdown", + "id": "gWR4Rvpmjq5T", + "metadata": { + "id": "gWR4Rvpmjq5T" + }, + "source": [ + "## Prerequisites 🏗️\n", + "\n", + "Before diving into the notebook, you need to:\n", + "\n", + "🔲 📚 **Study [What is Function-Calling](https://www.hf.co/learn/agents-course/bonus-unit1/what-is-function-calling) Section**\n", + "\n", + "🔲 📚 **Study [Fine-Tune your Model and what are LoRAs](https://www.hf.co/learn/agents-course/bonus-unit1/fine-tuning) Section**" + ] + }, + { + "cell_type": "markdown", + "id": "1rZXU_1wkEPu", + "metadata": { + "id": "1rZXU_1wkEPu" + }, + "source": [ + "# Step 0: Ask to Access Gemma on Hugging Face\n", + "\n", + "\"Gemma\"/\n", + "\n", + "\n", + "To access Gemma on Hugging Face:\n", + "\n", + "1. **Make sure you're signed in** to your Hugging Face Account\n", + "\n", + "2. Go to https://huggingface.co/google/gemma-2-2b-it\n", + "\n", + "3. Click on **Acknowledge license** and fill the form.\n", + "\n", + "Alternatively you can use another model, and modify the code accordingly (it can be a good exercise for you to be sure you know how to fine-tune for Function-Calling).\n", + "\n", + "You can use for instance:\n", + "\n", + "- [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)\n", + "\n", + "- [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)" + ] + }, + { + "cell_type": "markdown", + "id": "5hjyx9nJlvKG", + "metadata": { + "id": "5hjyx9nJlvKG" + }, + "source": [ + "## Step 1: Set the GPU 💪\n", + "\n", + "If you're on Colab:\n", + "\n", + "- To **accelerate the fine-tuning training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`\n", + "\n", + "\"GPU\n", + "\n", + "- `Hardware Accelerator > GPU`\n", + "\n", + "\"GPU\n", + "\n", + "\n", + "### Important\n", + "\n", + "For this Unit, **with the free-tier of Colab** it will take around **6h to train**.\n", + "\n", + "You have three solutions if you want to make it faster:\n", + "\n", + "1. Train on your computer if you have GPUs. It might take time but you have less risks of timeout.\n", + "\n", + "2. Use a Google Colab Pro that allows you use to A100 GPU (15-20min training).\n", + "\n", + "3. Just follow the code to learn how to do it without training." + ] + }, + { + "cell_type": "markdown", + "id": "5Thjsc9fj6Ej", + "metadata": { + "id": "5Thjsc9fj6Ej" + }, + "source": [ + "## Step 2: Install dependencies 📚\n", + "\n", + "We need multiple librairies:\n", + "\n", + "- `bitsandbytes` for quantization\n", + "- `peft`for LoRA adapters\n", + "- `Transformers`for loading the model\n", + "- `datasets`for loading and using the fine-tuning dataset\n", + "- `trl`for the trainer class" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e63f4962-c644-491e-aa91-50e453e953a4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e63f4962-c644-491e-aa91-50e453e953a4", + "outputId": "443077a6-7cff-4c46-90ac-bf279300f6ec", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -q -U bitsandbytes\n", + "%pip install -q -U peft\n", + "%pip install -q -U trl\n", + "%pip install -q -U tensorboardX\n", + "%pip install -q wandb" + ] + }, + { + "cell_type": "markdown", + "id": "UWNoZzi1urSZ", + "metadata": { + "id": "UWNoZzi1urSZ" + }, + "source": [ + "## Step 3: Create your Hugging Face Token to push your model to the Hub\n", + "\n", + "To be able to share your model with the community there are some more steps to follow:\n", + "\n", + "1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join\n", + "\n", + "2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.\n", + "\n", + "- Create a new token (https://huggingface.co/settings/tokens) **with write role**\n", + "\n", + "\"Create\n", + "\n", + "3️⃣ Store your token as an environment variable under the name \"HF_TOKEN\"\n", + "- **Be very carefull not to share it with others** !" + ] + }, + { + "cell_type": "markdown", + "id": "vBAkwg9zu6A1", + "metadata": { + "id": "vBAkwg9zu6A1" + }, + "source": [ + "## Step 4: Import the librairies\n", + "\n", + "Don't forget to put your HF token." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7ad2e4c2-593e-463e-9692-8d674c541d76", + "metadata": { + "id": "7ad2e4c2-593e-463e-9692-8d674c541d76", + "tags": [] + }, + "outputs": [], + "source": [ + "from enum import Enum\n", + "from functools import partial\n", + "import pandas as pd\n", + "import torch\n", + "import json\n", + "\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed\n", + "from datasets import load_dataset\n", + "from trl import SFTConfig, SFTTrainer\n", + "from peft import LoraConfig, TaskType\n", + "\n", + "seed = 42\n", + "set_seed(seed)\n", + "\n", + "import os\n", + "\n", + "# Put your HF Token here\n", + "os.environ['HF_TOKEN']=\"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\"" + ] + }, + { + "cell_type": "markdown", + "id": "44f30b2c-2cc0-48e0-91ca-4633e6444105", + "metadata": { + "id": "44f30b2c-2cc0-48e0-91ca-4633e6444105" + }, + "source": [ + "## Step 5: Processing the dataset into inputs\n", + "\n", + "In order to train the model, we need to **format the inputs into what we want the model to learn**.\n", + "\n", + "For this tutorial, I enhanced a popular dataset for function calling \"NousResearch/hermes-function-calling-v1\" by adding some new **thinking** step computer from **deepseek-ai/DeepSeek-R1-Distill-Qwen-32B**.\n", + "\n", + "But in order for the model to learn, we need **to format the conversation correctly**. If you followed Unit 1, you know that going from a list of messages to a prompt is handled by the **chat_template**, or, the default chat_template of gemma-2-2B does not contain tool calls. So we will need to modify it !\n", + "\n", + "This is the role of our **preprocess** function. To go from a list of messages, to a prompt that the model can understand.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "29da85c8-33bf-4864-aed7-733cbe703512", + "metadata": { + "id": "29da85c8-33bf-4864-aed7-733cbe703512", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating codeact split: 100%|██████████| 7139/7139 [00:00<00:00, 41534.73 examples/s]\n", + "Generating general split: 100%|██████████| 71246/71246 [00:01<00:00, 66171.53 examples/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " codeact: Dataset({\n", + " features: ['id', 'conversations'],\n", + " num_rows: 7139\n", + " })\n", + " general: Dataset({\n", + " features: ['id', 'conversations'],\n", + " num_rows: 71246\n", + " })\n", + "})" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# model_name = \"google/gemma-2-2b-it\"\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n", + "# dataset_name = \"Jofthomas/hermes-function-calling-thinking-V1\"\n", + "dataset_name = \"xingyaoww/code-act\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "\n", + "# tokenizer.chat_template\n", + "\n", + "# tokenizer.chat_template = \"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{{ '' + message['role'] + '\\n' + message['content'] | trim + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\\n'}}{% endif %}\"\n", + "\n", + "\n", + "# def preprocess(sample):\n", + "# messages = sample[\"messages\"]\n", + "# first_message = messages[0]\n", + "\n", + "# # Instead of adding a system message, we merge the content into the first user message\n", + "# if first_message[\"role\"] == \"system\":\n", + "# system_message_content = first_message[\"content\"]\n", + "# # Merge system content with the first user message\n", + "# messages[1][\"content\"] = system_message_content + \"Also, before making a call to a function take the time to plan the function to take. Make that thinking process between {your thoughts}\\n\\n\" + messages[1][\"content\"]\n", + "# # Remove the system message from the conversation\n", + "# messages.pop(0)\n", + "\n", + "# return {\"text\": tokenizer.apply_chat_template(messages, tokenize=False)}\n", + "\n", + "\n", + "\n", + "dataset = load_dataset(dataset_name)\n", + "# dataset = dataset.rename_column(\"conversations\", \"messages\")\n", + "\n", + "dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "90e6d825", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'openorca.n50000/cot.64934',\n", + " 'conversations': [{'content': 'You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-bystep and justify your answer.',\n", + " 'role': 'system'},\n", + " {'content': 'What was the question for this implicit rationale, and corresponding answer?\\nA white shirt is a different color from a black shirt.\\n The answer: no',\n", + " 'role': 'user'},\n", + " {'content': 'Question: Are white and black shirts the same color?\\nImplicit rationale: We are comparing the colors of white and black shirts.\\nAnswer: No, because a white shirt is a different color from a black shirt.',\n", + " 'role': 'assistant'}]}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[\"general\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "01575b67", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oct28_full6728/tabular/wiki_table_questions/nt-1710/claude-2/max5_p2+tool+cd',\n", + " 'conversations': [{'content': 'You are a helpful assistant assigned with the task of problem-solving. To achieve this, you will be using an interactive coding environment equipped with a variety of tool functions to assist you throughout the process.\\n\\nAt each turn, you should first provide your step-by-step thinking for solving the task. After that, you have two options:\\n\\n1) Interact with a Python programming environment and receive the corresponding output. Your code should be enclosed using \"\" tag, for example: print(\"Hello World!\") .\\n2) Directly provide a solution that adheres to the required format for the given task. Your solution should be enclosed using \"\" tag, for example: The answer is A .\\n\\nYou have 5 chances to interact with the environment or propose a solution. You can only propose a solution 2 times.',\n", + " 'role': 'system'},\n", + " {'content': 'Task:\\nGiven the following table (only the first 5 rows are shown):\\n City Country IATA ICAO Airport Commenced Ended\\n0 Bandung Indonesia BDO WICC Husein Sastranegara International Airport 2013 present\\n1 Bangalore India BLR VOBL Bengaluru International Airport 2008[N] present[N]\\n2 Bangkok Thailand BKK VTBS Suvarnabhumi Airport 2004 present\\n3 Cebu Philippines CEB RPVM Mactan-Cebu International Airport 2011 present\\n4 Chennai India MAA VOMM Chennai International Airport 2007 present\\n\\nWrite a SQL query to find the answer to the question: how long did tigerair fly into chiang mai?.\\nThe SQLite3 database is preloaded for you and can be accessed within block via the variable `conn` (SQLite3 connection object).\\nThe table name is `data_table`.',\n", + " 'role': 'user'},\n", + " {'content': 'To find how long tigerair flew into Chiang Mai, I need to:\\n1. Filter the table to only include rows for Chiang Mai airport \\n2. Select the \\'Commenced\\' and \\'Ended\\' columns\\n3. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced \\nFROM data_table\\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\nended = result[0] \\ncommenced = result[1]\\n\\nprint(ended, commenced)\\n\\n\\nThe duration is 2015 - 2012 = 3 years',\n", + " 'role': 'assistant'},\n", + " {'content': 'Observation:\\nYour answer is wrong.\\nYou have 4 steps left and 1 chances to propose solution left.',\n", + " 'role': 'user'},\n", + " {'content': 'My previous solution was incorrect. Let me think through this again:\\n\\n1. I need to filter the table to only rows for Chiang Mai airport\\n2. Select the \\'Commenced\\' and \\'Ended\\' columns \\n3. The \\'Commenced\\' and \\'Ended\\' columns contain years, so I need to convert them to dates\\n4. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\nfrom datetime import datetime\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced\\nFROM data_table \\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\n\\nended = datetime.strptime(result[0], \\'%Y\\')\\ncommenced = datetime.strptime(result[1], \\'%Y\\')\\n\\nduration = ended - commenced\\n\\nprint(duration)\\n',\n", + " 'role': 'assistant'},\n", + " {'content': 'Observation:\\n1096 days, 0:00:00\\nYou have 3 steps left and 1 chances to propose solution left.',\n", + " 'role': 'user'},\n", + " {'content': '\\n3 years\\n', 'role': 'assistant'}]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[\"codeact\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c3e5c04c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'content': 'You are a helpful assistant assigned with the task of problem-solving. To achieve this, you will be using an interactive coding environment equipped with a variety of tool functions to assist you throughout the process.\\n\\nAt each turn, you should first provide your step-by-step thinking for solving the task. After that, you have two options:\\n\\n1) Interact with a Python programming environment and receive the corresponding output. Your code should be enclosed using \"\" tag, for example: print(\"Hello World!\") .\\n2) Directly provide a solution that adheres to the required format for the given task. Your solution should be enclosed using \"\" tag, for example: The answer is A .\\n\\nYou have 5 chances to interact with the environment or propose a solution. You can only propose a solution 2 times.',\n", + " 'role': 'system'},\n", + " {'content': 'Task:\\nGiven the following table (only the first 5 rows are shown):\\n City Country IATA ICAO Airport Commenced Ended\\n0 Bandung Indonesia BDO WICC Husein Sastranegara International Airport 2013 present\\n1 Bangalore India BLR VOBL Bengaluru International Airport 2008[N] present[N]\\n2 Bangkok Thailand BKK VTBS Suvarnabhumi Airport 2004 present\\n3 Cebu Philippines CEB RPVM Mactan-Cebu International Airport 2011 present\\n4 Chennai India MAA VOMM Chennai International Airport 2007 present\\n\\nWrite a SQL query to find the answer to the question: how long did tigerair fly into chiang mai?.\\nThe SQLite3 database is preloaded for you and can be accessed within block via the variable `conn` (SQLite3 connection object).\\nThe table name is `data_table`.',\n", + " 'role': 'user'},\n", + " {'content': 'To find how long tigerair flew into Chiang Mai, I need to:\\n1. Filter the table to only include rows for Chiang Mai airport \\n2. Select the \\'Commenced\\' and \\'Ended\\' columns\\n3. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced \\nFROM data_table\\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\nended = result[0] \\ncommenced = result[1]\\n\\nprint(ended, commenced)\\n\\n\\nThe duration is 2015 - 2012 = 3 years',\n", + " 'role': 'assistant'},\n", + " {'content': 'Observation:\\nYour answer is wrong.\\nYou have 4 steps left and 1 chances to propose solution left.',\n", + " 'role': 'user'},\n", + " {'content': 'My previous solution was incorrect. Let me think through this again:\\n\\n1. I need to filter the table to only rows for Chiang Mai airport\\n2. Select the \\'Commenced\\' and \\'Ended\\' columns \\n3. The \\'Commenced\\' and \\'Ended\\' columns contain years, so I need to convert them to dates\\n4. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\nfrom datetime import datetime\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced\\nFROM data_table \\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\n\\nended = datetime.strptime(result[0], \\'%Y\\')\\ncommenced = datetime.strptime(result[1], \\'%Y\\')\\n\\nduration = ended - commenced\\n\\nprint(duration)\\n',\n", + " 'role': 'assistant'},\n", + " {'content': 'Observation:\\n1096 days, 0:00:00\\nYou have 3 steps left and 1 chances to propose solution left.',\n", + " 'role': 'user'},\n", + " {'content': '\\n3 years\\n', 'role': 'assistant'}]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversation = dataset[\"codeact\"][0][\"conversations\"]\n", + "conversation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "91ebe404", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method apply_chat_template in module transformers.tokenization_utils_base:\n", + "\n", + "apply_chat_template(conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]], tools: Optional[List[Union[Dict, Callable]]] = None, documents: Optional[List[Dict[str, str]]] = None, chat_template: Optional[str] = None, add_generation_prompt: bool = False, continue_final_message: bool = False, tokenize: bool = True, padding: Union[bool, str, transformers.utils.generic.PaddingStrategy] = False, truncation: bool = False, max_length: Optional[int] = None, return_tensors: Union[str, transformers.utils.generic.TensorType, NoneType] = None, return_dict: bool = False, return_assistant_tokens_mask: bool = False, tokenizer_kwargs: Optional[Dict[str, Any]] = None, **kwargs) -> Union[str, List[int], List[str], List[List[int]], transformers.tokenization_utils_base.BatchEncoding] method of transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast instance\n", + " Converts a list of dictionaries with `\"role\"` and `\"content\"` keys to a list of token\n", + " ids. This method is intended for use with chat models, and will read the tokenizer's chat_template attribute to\n", + " determine the format and control tokens to use when converting.\n", + " \n", + " Args:\n", + " conversation (Union[List[Dict[str, str]], List[List[Dict[str, str]]]]): A list of dicts\n", + " with \"role\" and \"content\" keys, representing the chat history so far.\n", + " tools (`List[Dict]`, *optional*):\n", + " A list of tools (callable functions) that will be accessible to the model. If the template does not\n", + " support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema,\n", + " giving the name, description and argument types for the tool. See our\n", + " [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use)\n", + " for more information.\n", + " documents (`List[Dict[str, str]]`, *optional*):\n", + " A list of dicts representing documents that will be accessible to the model if it is performing RAG\n", + " (retrieval-augmented generation). If the template does not support RAG, this argument will have no\n", + " effect. We recommend that each document should be a dict containing \"title\" and \"text\" keys. Please\n", + " see the RAG section of the [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#arguments-for-RAG)\n", + " for examples of passing documents with chat templates.\n", + " chat_template (`str`, *optional*):\n", + " A Jinja template to use for this conversion. It is usually not necessary to pass anything to this\n", + " argument, as the model's template will be used by default.\n", + " add_generation_prompt (bool, *optional*):\n", + " If this is set, a prompt with the token(s) that indicate\n", + " the start of an assistant message will be appended to the formatted output. This is useful when you want to generate a response from the model.\n", + " Note that this argument will be passed to the chat template, and so it must be supported in the\n", + " template for this argument to have any effect.\n", + " continue_final_message (bool, *optional*):\n", + " If this is set, the chat will be formatted so that the final\n", + " message in the chat is open-ended, without any EOS tokens. The model will continue this message\n", + " rather than starting a new one. This allows you to \"prefill\" part of\n", + " the model's response for it. Cannot be used at the same time as `add_generation_prompt`.\n", + " tokenize (`bool`, defaults to `True`):\n", + " Whether to tokenize the output. If `False`, the output will be a string.\n", + " padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n", + " Select a strategy to pad the returned sequences (according to the model's padding side and padding\n", + " index) among:\n", + " \n", + " - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", + " sequence if provided).\n", + " - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n", + " acceptable input length for the model if that argument is not provided.\n", + " - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n", + " lengths).\n", + " truncation (`bool`, defaults to `False`):\n", + " Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.\n", + " max_length (`int`, *optional*):\n", + " Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If\n", + " not specified, the tokenizer's `max_length` attribute will be used as a default.\n", + " return_tensors (`str` or [`~utils.TensorType`], *optional*):\n", + " If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable\n", + " values are:\n", + " - `'tf'`: Return TensorFlow `tf.Tensor` objects.\n", + " - `'pt'`: Return PyTorch `torch.Tensor` objects.\n", + " - `'np'`: Return NumPy `np.ndarray` objects.\n", + " - `'jax'`: Return JAX `jnp.ndarray` objects.\n", + " return_dict (`bool`, defaults to `False`):\n", + " Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.\n", + " tokenizer_kwargs (`Dict[str: Any]`, *optional*): Additional kwargs to pass to the tokenizer.\n", + " return_assistant_tokens_mask (`bool`, defaults to `False`):\n", + " Whether to return a mask of the assistant generated tokens. For tokens generated by the assistant,\n", + " the mask will contain 1. For user and system tokens, the mask will contain 0.\n", + " This functionality is only available for chat templates that support it via the `{% generation %}` keyword.\n", + " **kwargs: Additional kwargs to pass to the template renderer. Will be accessible by the chat template.\n", + " \n", + " Returns:\n", + " `Union[List[int], Dict]`: A list of token ids representing the tokenized chat so far, including control tokens. This\n", + " output is ready to pass to the model, either directly or via methods like `generate()`. If `return_dict` is\n", + " set, will return a dict of tokenizer outputs instead.\n", + "\n" + ] + } + ], + "source": [ + "help(tokenizer.apply_chat_template) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e4239d40", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'<|im_start|>system\\nYou are a helpful assistant assigned with the task of problem-solving. To achieve this, you will be using an interactive coding environment equipped with a variety of tool functions to assist you throughout the process.\\n\\nAt each turn, you should first provide your step-by-step thinking for solving the task. After that, you have two options:\\n\\n1) Interact with a Python programming environment and receive the corresponding output. Your code should be enclosed using \"\" tag, for example: print(\"Hello World!\") .\\n2) Directly provide a solution that adheres to the required format for the given task. Your solution should be enclosed using \"\" tag, for example: The answer is A .\\n\\nYou have 5 chances to interact with the environment or propose a solution. You can only propose a solution 2 times.<|im_end|>\\n<|im_start|>user\\nTask:\\nGiven the following table (only the first 5 rows are shown):\\n City Country IATA ICAO Airport Commenced Ended\\n0 Bandung Indonesia BDO WICC Husein Sastranegara International Airport 2013 present\\n1 Bangalore India BLR VOBL Bengaluru International Airport 2008[N] present[N]\\n2 Bangkok Thailand BKK VTBS Suvarnabhumi Airport 2004 present\\n3 Cebu Philippines CEB RPVM Mactan-Cebu International Airport 2011 present\\n4 Chennai India MAA VOMM Chennai International Airport 2007 present\\n\\nWrite a SQL query to find the answer to the question: how long did tigerair fly into chiang mai?.\\nThe SQLite3 database is preloaded for you and can be accessed within block via the variable `conn` (SQLite3 connection object).\\nThe table name is `data_table`.<|im_end|>\\n<|im_start|>assistant\\nTo find how long tigerair flew into Chiang Mai, I need to:\\n1. Filter the table to only include rows for Chiang Mai airport \\n2. Select the \\'Commenced\\' and \\'Ended\\' columns\\n3. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced \\nFROM data_table\\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\nended = result[0] \\ncommenced = result[1]\\n\\nprint(ended, commenced)\\n\\n\\nThe duration is 2015 - 2012 = 3 years<|im_end|>\\n<|im_start|>user\\nObservation:\\nYour answer is wrong.\\nYou have 4 steps left and 1 chances to propose solution left.<|im_end|>\\n<|im_start|>assistant\\nMy previous solution was incorrect. Let me think through this again:\\n\\n1. I need to filter the table to only rows for Chiang Mai airport\\n2. Select the \\'Commenced\\' and \\'Ended\\' columns \\n3. The \\'Commenced\\' and \\'Ended\\' columns contain years, so I need to convert them to dates\\n4. Calculate the difference between the \\'Ended\\' and \\'Commenced\\' dates to get the duration\\n\\n\\nimport sqlite3\\nfrom datetime import datetime\\n\\nconn = sqlite3.connect(\\':memory:\\')\\n\\nconn.execute(\\'\\'\\'CREATE TABLE data_table\\n (City text, Country text, IATA text, ICAO text, Airport text, Commenced text, Ended text)\\'\\'\\')\\n\\nconn.execute(\"INSERT INTO data_table VALUES (\\'Chiang Mai\\', \\'Thailand\\', \\'CNX\\', \\'VTCC\\', \\'Chiang Mai International Airport\\', \\'2012\\', \\'2015\\')\")\\n\\nquery = \"\"\"\\nSELECT Ended, Commenced\\nFROM data_table \\nWHERE City = \\'Chiang Mai\\'\\n\"\"\"\\n\\nresult = conn.execute(query).fetchone()\\n\\nended = datetime.strptime(result[0], \\'%Y\\')\\ncommenced = datetime.strptime(result[1], \\'%Y\\')\\n\\nduration = ended - commenced\\n\\nprint(duration)\\n<|im_end|>\\n<|im_start|>user\\nObservation:\\n1096 days, 0:00:00\\nYou have 3 steps left and 1 chances to propose solution left.<|im_end|>\\n<|im_start|>assistant\\n\\n3 years\\n<|im_end|>\\n'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.apply_chat_template(conversation, tokenize=False)" + ] + }, + { + "cell_type": "markdown", + "id": "dc8736d5-d64b-4c5c-9738-be08421d3f95", + "metadata": { + "id": "dc8736d5-d64b-4c5c-9738-be08421d3f95" + }, + "source": [ + "## Step 6: A Dedicated Dataset for This Unit\n", + "\n", + "For this Bonus Unit, we created a custom dataset based on [NousResearch/hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1), which is considered a **reference** when it comes to function-calling datasets.\n", + "\n", + "While the original dataset is excellent, it does **not** include a **“thinking”** step.\n", + "\n", + "In Function-Calling, such a step is optional, but recent work—like the **deepseek** model or the paper [\"Test-Time Compute\"](https://huggingface.co/papers/2408.03314)—suggests that giving an LLM time to “think” before it answers (or in this case, **before** taking an action) can **significantly improve** model performance.\n", + "\n", + "I, decided to then compute a subset of this dataset and to give it to [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) in order to compute some thinking tokens `` before any function call. Which resulted in the following dataset :\n", + "![Input Dataset](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/bonus-unit1/dataset_function_call.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b63d4832-d92e-482d-9fe6-6e9dbfee377a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b63d4832-d92e-482d-9fe6-6e9dbfee377a", + "outputId": "547d58fc-cf84-4878-f66e-ae817030a251", + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b2ee958727314c8186af7ee5e5da64aa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/3570 [00:00` then the user query, here: `\"Can you get me the latest news headlines for the United States?\"`\n", + "\n", + "2. An *Assistant message* here called \"model\" to fit the criterias from gemma models containing two new phases, a **\"thinking\"** phase contained in `` and an **\"Act\"** phase contained in ``.\n", + "\n", + "3. If the model contains a ``, we will append the result of this action in a new **\"Tool\"** message containing a `` with the answer from the tool." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dc60da04-9411-487a-b629-2c59024a20c0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dc60da04-9411-487a-b629-2c59024a20c0", + "outputId": "8af76a10-5a72-4401-cdf5-ee047fc2d850", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "human\n", + "You are a function calling AI model. You are provided with function signatures within XML tags.You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.Here are the available tools: [{'type': 'function', 'function': {'name': 'get_news_headlines', 'description': 'Get the latest news headlines', 'parameters': {'type': 'object', 'properties': {'country': {'type': 'string', 'description': 'The country for which headlines are needed'}}, 'required': ['country']}}}, {'type': 'function', 'function': {'name': 'search_recipes', 'description': 'Search for recipes based on ingredients', 'parameters': {'type': 'object', 'properties': {'ingredients': {'type': 'array', 'items': {'type': 'string'}, 'description': 'The list of ingredients'}}, 'required': ['ingredients']}}}] Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within XML tags as follows:\n", + "\n", + "{tool_call}\n", + "Also, before making a call to a function take the time to plan the function to take. Make that thinking process between {your thoughts}\n", + "\n", + "Can you get me the latest news headlines for the United States?\n", + "model\n", + "Okay, so the user asked for the latest news headlines in the United States. I need to figure out how to respond appropriately using the tools available.\n", + "\n", + "Looking at the tools provided, there's a function called get_news_headlines. That seems perfect for this situation. It requires a country code, which the user specified as the United States.\n", + "\n", + "Since the user is asking specifically about the US, I'll use the country parameter as 'United States' in the function call. This should fetch the relevant news for them.\n", + "\n", + "I should make sure to format the response correctly as a JSON object with the function name and arguments.\n", + "\n", + "{'name': 'get_news_headlines', 'arguments': {'country': 'United States'}}\n", + "\n", + "tool\n", + "\n", + "{'headlines': ['US President signs new bill into law', 'Major earthquake hits California', 'NASA announces new Mars mission', 'Tech giant releases new smartphone', 'US economy shows signs of recovery']}\n", + "\n", + "model\n", + "Here are the latest news headlines for the United States:\n", + "1. US President signs new bill into law\n", + "2. Major earthquake hits California\n", + "3. NASA announces new Mars mission\n", + "4. Tech giant releases new smartphone\n", + "5. US economy shows signs of recovery\n", + "human\n", + "That's interesting. Can you get me the news for France now?\n", + "model\n", + "Okay, the user initially asked for the latest US news, so I used get_news_headlines with 'United States'. Now the user is asking for France, so I'll call the same function with 'France' as the country. That makes sense because the function is designed for any country.\n", + "\n", + "{'name': 'get_news_headlines', 'arguments': {'country': 'France'}}\n", + "\n", + "tool\n", + "\n", + "{'headlines': ['French President announces new environmental policy', 'Paris Fashion Week highlights', 'France wins World Cup qualifier', 'New culinary trend sweeps across France', 'French tech startup raises millions in funding']}\n", + "\n", + "model\n", + "Here are the latest news headlines for France:\n", + "1. French President announces new environmental policy\n", + "2. Paris Fashion Week highlights\n", + "3. France wins World Cup qualifier\n", + "4. New culinary trend sweeps across France\n", + "5. French tech startup raises millions in funding\n", + "\n" + ] + } + ], + "source": [ + "# Let's look at how we formatted the dataset\n", + "print(dataset[\"train\"][8][\"text\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "53a48281-2346-4dfb-ad60-cad85129ec9b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "53a48281-2346-4dfb-ad60-cad85129ec9b", + "outputId": "e16c07f9-8bbf-4c82-d2d3-41d96a95ab2e", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "# Sanity check\n", + "print(tokenizer.pad_token)\n", + "print(tokenizer.eos_token)" + ] + }, + { + "cell_type": "markdown", + "id": "d6864b36-6033-445a-b6e2-b6bb02e38e26", + "metadata": { + "id": "d6864b36-6033-445a-b6e2-b6bb02e38e26" + }, + "source": [ + "## Step 8: Let's Modify the Tokenizer\n", + "\n", + "Indeed, as we saw in Unit 1, the tokenizer splits text into sub-words by default. This is **not** what we want for our new special tokens!\n", + "\n", + "While we segmented our example using ``, ``, and ``, the tokenizer does **not** yet treat them as whole tokens—it still tries to break them down into smaller pieces. To ensure the model correctly interprets our new format, we must **add these tokens** to our tokenizer.\n", + "\n", + "Additionally, since we changed the `chat_template` in our **preprocess** function to format conversations as messages within a prompt, we also need to modify the `chat_template` in the tokenizer to reflect these changes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "833ba5d6-4c1e-4689-9fed-22cc03d2a63a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 754, + "referenced_widgets": [ + "54fc2d9962de4ffda0500a01e112b58a", + "8e70cccb0fb04a43b3c6ab234bcae9a8", + "f2d87447fe8448c7baff656c540620e6", + "5f438fbbfb60436faf79662e81092154", + "197619158cd247649eac3722284c8d19", + "1d104fe8612f45e4810bbed7e3540330", + "d62425b0d0ea497ead2cd981eaa61c7e", + "2828b2455ec44143a5739c8e8207fb9b", + "03b839beea6a4f6b8d9005491146615d", + "0db5d5c516d7479ebf7603093f9e1e99", + "654fd18a9e59415e9dd314dc2f94a654", + "deba87fc28db4f78b58fc1e456fea335", + "f06414fa35b64ff588511796c9b51d1f", + "21cf377576154ffeb028f4ae3b3503d5", + "5c8fa13e559942849a61d9480be730bd", + "fae24e4a14814c7ebef30b422c2d3652", + "1981ff353e494735be4706a76053c24a", + "9a8b33ef0f6c446496cc7dae07da321c", + "2b8c9e43da4e4e008a2a2b06e6f3106e", + "62f3a32c4f484dd6899ba5d6ffbaca11", + "1e271fcb92a14680b84c33cb7c28f9c0", + "da543c28b9e44723b019220de8427b5e", + "ce3c69ec86224ffea8c46deb20831bb7", + "3bb2805fcf0441089c0f4789e28b072d", + "bc6e638133f545ff8d54f767de0cb35a", + "6e1393f9f39a4b3cb94753c883b1bbf7", + "a76145e51f7544fdabd23198baf318c4", + "eedc1213dd604bbe827dcec617fdae34", + "f73f91bb567a48f890ba0827b7ed321f", + "a8edc3a5b0d9422b8e2e63d5564cfdc9", + "e08c970bbf4540a89533a43f719f68e4", + "fe30dac330db4fde94af1c54c84a1be8", + "4cfce958cf5445129ab6281784664038", + "71ba28670e8d4583b66b1c1587cdbce1", + "4349fab6f16343cb8955367f6fbc4a43", + "6296f0c3ac5647e9b9cbc7c8212a2b35", + "c6d97ccbecb0407f89ef4469f3874e74", + "ccabb079b6fd466fb877a018aebf793c", + "0ff4d8414af94af59628b0417a40cc7d", + "534a5dc9cd7e475db2be613849959fc7", + "b821e13722e24feb941fa31a604a85e9", + "91c21b2b127640e195f2e7c7256b5a0e", + "1296a8a4baea4faabc62a42b2ca1a53a", + "13b49790234c4d1aa8fe9b532fcf95bf", + "e1ef7211c5554b2f8fdd39f2f846d2d9", + "9ac674eeba0542e7b4e28a13e90ce652", + "cc4993f1af6b4f2685ecbced238b8913", + "5ec98ede3fbf4c64843b935ce3ad28a7", + "bdbd9e7e1d674edeab47e3595f6af4f8", + "2eb5230a43e84790b0392db58157b0f1", + "26bd63db6a384c12b7d42bf95ca1d1bc", + "6f4e106bb5b44f398a1fe4ac588f6cca", + "0c881b9322624d70bb2c5147a5422633", + "ec8ecb8bdee4435fbc97d73863ebd9f8", + "1af481e6544a4fa386815218461cd5c3", + "2f73c87217c84bd293a5ea5b4aba0ae5", + "6883c5e46d3948649488d3e9f81ddb9c", + "d0498872e00f4644847e073c6000a111", + "482f477cc2354d9da6a8c2932bb73e98", + "1c810f5364544762a3093e1a0a9c1e5b", + "681019ca1d084d9692f5085d1df520ae", + "b1bb9bcec30144a380fa4fe440c41d7c", + "425fe267a38748ea9670a0996b802bdc", + "95eebbfed6fc41d6a8c668abe1608756", + "51b42c72bc2b428fbc0389bfd4d9e598", + "611a6d951d2a42b9b650496612cfd484", + "a893802af41d4400b35b9cb71add1387", + "45e867ccde7d4b5bb4ed4861d723a758", + "f5e196c908e7491982773d3231fff3e6", + "68baf2700f614be288bf35e54d207096", + "001b32600fdd418bb30c6b5ff85e269c", + "2bcef869f1814a04bbbed73df7a9ab24", + "e3bd52cd621e4d2196e94327324722c4", + "1e9d8fccba9a468291ffe271b3497830", + "d1d376228c334c5999143b905e234ffc", + "fc9a32511862493e980682b6ff5044bb", + "538f6a3c632f431fa9d16ab17383a602" + ] + }, + "id": "833ba5d6-4c1e-4689-9fed-22cc03d2a63a", + "outputId": "3138107e-a2cd-447d-ee69-e07e401a1540", + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bedabbdf4cf44865a83fc9702391c298", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00\"\n", + " eotools = \"\"\n", + " think = \"\"\n", + " eothink = \"\"\n", + " tool_call=\"\"\n", + " eotool_call=\"\"\n", + " tool_response=\"\"\n", + " eotool_response=\"\"\n", + " pad_token = \"\"\n", + " eos_token = \"\"\n", + " @classmethod\n", + " def list(cls):\n", + " return [c.value for c in cls]\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\n", + " model_name,\n", + " pad_token=ChatmlSpecialTokens.pad_token.value,\n", + " additional_special_tokens=ChatmlSpecialTokens.list()\n", + " )\n", + "tokenizer.chat_template = \"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{{ '' + message['role'] + '\\n' + message['content'] | trim + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\\n'}}{% endif %}\"\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(model_name,\n", + " attn_implementation='eager',\n", + " device_map=\"auto\")\n", + "model.resize_token_embeddings(len(tokenizer))\n", + "model.to(torch.bfloat16)\n" + ] + }, + { + "cell_type": "markdown", + "id": "X6DBY8AqxFLL", + "metadata": { + "id": "X6DBY8AqxFLL" + }, + "source": [ + "## Step 9: Let's configure the LoRA\n", + "\n", + "This is we are going to define the parameter of our adapter. Those a the most important parameters in LoRA as they define the size and importance of the adapters we are training." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "482d36ab-e326-4fd7-bc59-425abcca55e7", + "metadata": { + "id": "482d36ab-e326-4fd7-bc59-425abcca55e7", + "tags": [] + }, + "outputs": [], + "source": [ + "from peft import LoraConfig\n", + "\n", + "# TODO: Configure LoRA parameters\n", + "# r: rank dimension for LoRA update matrices (smaller = more compression)\n", + "rank_dimension = 16\n", + "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n", + "lora_alpha = 64\n", + "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n", + "lora_dropout = 0.05\n", + "\n", + "peft_config = LoraConfig(r=rank_dimension,\n", + " lora_alpha=lora_alpha,\n", + " lora_dropout=lora_dropout,\n", + " target_modules=[\"gate_proj\",\"q_proj\",\"lm_head\",\"o_proj\",\"k_proj\",\"embed_tokens\",\"down_proj\",\"up_proj\",\"v_proj\"], # wich layer in the transformers do we target ?\n", + " task_type=TaskType.CAUSAL_LM)" + ] + }, + { + "cell_type": "markdown", + "id": "zdDR9hzgxPu2", + "metadata": { + "id": "zdDR9hzgxPu2" + }, + "source": [ + "## Step 10: Let's define the Trainer and the Fine-Tuning hyperparameters\n", + "\n", + "In this step, we define the Trainer, the class that we use to fine-tune our model and the hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3598b688-5a6f-437f-95ac-4794688cd38f", + "metadata": { + "id": "3598b688-5a6f-437f-95ac-4794688cd38f", + "tags": [] + }, + "outputs": [], + "source": [ + "username=\"Jofthomas\"# REPLCAE with your Hugging Face username\n", + "output_dir = \"gemma-2-2B-it-thinking-function_calling-V0\" # The directory where the trained model checkpoints, logs, and other artifacts will be saved. It will also be the default name of the model when pushed to the hub if not redefined later.\n", + "per_device_train_batch_size = 1\n", + "per_device_eval_batch_size = 1\n", + "gradient_accumulation_steps = 4\n", + "logging_steps = 5\n", + "learning_rate = 1e-4 # The initial learning rate for the optimizer.\n", + "\n", + "max_grad_norm = 1.0\n", + "num_train_epochs=1\n", + "warmup_ratio = 0.1\n", + "lr_scheduler_type = \"cosine\"\n", + "max_seq_length = 1500\n", + "\n", + "training_arguments = SFTConfig(\n", + " output_dir=output_dir,\n", + " per_device_train_batch_size=per_device_train_batch_size,\n", + " per_device_eval_batch_size=per_device_eval_batch_size,\n", + " gradient_accumulation_steps=gradient_accumulation_steps,\n", + " save_strategy=\"no\",\n", + " eval_strategy=\"epoch\",\n", + " logging_steps=logging_steps,\n", + " learning_rate=learning_rate,\n", + " max_grad_norm=max_grad_norm,\n", + " weight_decay=0.1,\n", + " warmup_ratio=warmup_ratio,\n", + " lr_scheduler_type=lr_scheduler_type,\n", + " report_to=\"tensorboard\",\n", + " bf16=True,\n", + " hub_private_repo=False,\n", + " push_to_hub=False,\n", + " num_train_epochs=num_train_epochs,\n", + " gradient_checkpointing=True,\n", + " gradient_checkpointing_kwargs={\"use_reentrant\": False},\n", + " packing=True,\n", + " max_seq_length=max_seq_length,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "59TTqmW2xmV2", + "metadata": { + "id": "59TTqmW2xmV2" + }, + "source": [ + "As Trainer, we use the `SFTTrainer` which is a Supervised Fine-Tuning Trainer." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ba0366b5-c9d0-4f7e-97e0-1f964cfad147", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 263, + "referenced_widgets": [ + "e3fe61834c3e49a3895212a336776f9d", + "472cbf91b24f46829960d68e2316c417", + "bd746ea2e46a491e954ac6f32fb0e45b", + "b1542891fc6243d98d51981dd0584bdf", + "b2e16ad7540d4760b28f3a8c419905f8", + "f2157c83879046a29b72613bce9de56e", + "3b07ec7f4d024b61abe94c8adeebed55", + "e6f810bd430245b190ee932554cca05c", + "952893c941c346c2aedcd9358859a3b9", + "d36e7dd4b9dc497faa6e8f63843a738f", + "f469ec8c79ac476c82a5e228f347bffa", + "e9de72aadc5743a2b56537b3ad035461", + "57d137229091486dbf0e4b7dd6dce98a", + "4d006dc72b2b45b58caf4d398c3756b8", + "36bbd4bd563a4053a7af8532300253b7", + "cacc3c3a10c64b338866a8e42201e44c", + "b4c43d908bd64d9bbdb488dac46d2e45", + "96691a6287ef401582a2a1744a4940c4", + "328fa6d902bd46bbb0ecdc7404a13e8c", + "f375fed157034dfcbd28744027d77eba", + "33ec043a635f4e99b67cd6f7e6fc6193", + "39fee7a249c146d1a166e58755c1cda8", + "2ccddb85840c4981ae089ae4c74a2de6", + "89eee480405a416ba0edf097423724b9", + "56e8079c374a4f3f9af5ef96a73f2955", + "3aeb28bc164444d7afb7bf7435a001f2", + "1ead218e71614374909b92fda097fd42", + "822bea0ac84d4ff29d984bf5f5d2c3a2", + "ecef440c871b4daba34661a1ddba6b0c", + "e633387b1640461e82617c1702ee82f5", + "26484831a87a4b489e1288ea71ea7767", + "9f8d631358e240f982e31171d1bd9f26", + "a0c7029819414c5dacefed93194cd763", + "f4a01e54ec53475585eaa88b3a272b4b", + "63c269b37eed4d348f9ce24eef15fc15", + "a89230859593424e960047a96977c6b8", + "91163fcffc60438cb39b0eb586dac418", + "89033e9c0dd249db9dc9a3b1e215dded", + "73c9d510c8754f2ea21adf318e35bc8e", + "0fe7751f55134695afb44bf8673dd4d9", + "f7dd34e15348462297564f0e6e0b568d", + "537e188c000041fea6adf26f2255d738", + "39fff32b9756437581228465165a3115", + "4f75329d3e8d4cc38a405c1c4cc51d70", + "8bbe22288edf4a06b2c56952fd81d5e7", + "298f092855f14af79ec2eda792732810", + "2e6392b95f8a48568a89780adf76387f", + "55e72b7f262b4f57a6abb1c9f01c8de2", + "9fbca7fa0d6b4fff910b806a97fa7718", + "a385bba49b514ab386cb5f4cbb01821f", + "78a34eb9cd534c3d84c8f22d3c53c88f", + "cee71fbbf8a04b3bb64b96e7fa2b0b0e", + "0a0bc95f445948f486fbce865a4642f2", + "b782092e2282488ba86f85eebe697603", + "f7ba9e0f4e64484a82374bb5f1d12b15", + "39c0963803c74ebab07cec20e10d0184", + "46b69fb951af44f99232f459daa4f103", + "3e23b4c5848843fcb44dc0ae2f157d66", + "c9f86634a6bf4e49a902e3d42e67f1bd", + "03b8a027c56f4b52a3e54f57bb5bb526", + "58a18918bae34aca8ec73ae89fd5bc24", + "fd6e1776cbcd4f7b96ec6d9754eb2c83", + "b5121cada3514b67a9c533e7468b3058", + "bf70daf78057419b8a78af75a093a3dd", + "e0b3b3c072be44de8e0a2dae91598aa6", + "4e21f1bd903443a89dea32bb3f3c26a9" + ] + }, + "id": "ba0366b5-c9d0-4f7e-97e0-1f964cfad147", + "outputId": "5602ad04-17c4-431b-e20d-7f0d0c7fd24e", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/miniconda/lib/python3.9/site-packages/peft/tuners/tuners_utils.py:543: UserWarning: Model with `tie_word_embeddings=True` and the tied_target_modules=['lm_head'] are part of the adapter. This can lead to complications, for example when merging the adapter or converting your model to formats other than safetensors. See for example https://github.com/huggingface/peft/issues/2018.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "be2be97d4aa64b23beaf316024229a3b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Applying chat template to train dataset: 0%| | 0/100 [00:00\n", + " \n", + " \n", + " [12/12 00:24, Epoch 1/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation Loss
11.2368001.240833

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/miniconda/lib/python3.9/site-packages/peft/utils/save_and_load.py:230: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n", + " warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n" + ] + } + ], + "source": [ + "trainer.train()\n", + "trainer.save_model()" + ] + }, + { + "cell_type": "markdown", + "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393", + "metadata": { + "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393", + "tags": [] + }, + "source": [ + "## Step 11: Let's push the Model and the Tokenizer to the Hub\n", + "\n", + "Let's push our model and out tokenizer to the Hub ! The model will be pushed under your username + the output_dir that we specified earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "370af020-9319-4ff7-bea1-2842a4847caa", + "metadata": { + "id": "370af020-9319-4ff7-bea1-2842a4847caa", + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00c6c786a8014635952d94bb505923f1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "events.out.tfevents.1739887545.r-jofthomas-fttest-kff5bkw4-24c03-yhiku: 0%| | 0.00/6.88k [00:00\"\n", + "# push the tokenizer to hub ( replace with your username and your previously specified\n", + "tokenizer.push_to_hub(f\"{username}/{output_dir}\", token=True)" + ] + }, + { + "cell_type": "markdown", + "id": "76d275ce-a3e6-4d30-8d8c-0ee274de5370", + "metadata": { + "id": "76d275ce-a3e6-4d30-8d8c-0ee274de5370" + }, + "source": [ + "## Step 12: Let's now test our model !\n", + "\n", + "To so, we will :\n", + "\n", + "1. Load the adapter from the hub !\n", + "2. Load the base model : **\"google/gemma-2-2b-it\"** from the hub\n", + "3. Resize the model to with the new tokens we introduced !" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "56b89825-70ac-42c1-934c-26e2d54f3b7b", + "metadata": { + "colab": { + "referenced_widgets": [ + "390c54434b6448b988ce015eeafe34c9", + "35b2fe2d357b46488ccef710f2a9bfd7", + "9c313149d4324bdaa9c8ddc373964d18" + ] + }, + "id": "56b89825-70ac-42c1-934c-26e2d54f3b7b", + "outputId": "a4cd00b8-61fa-4522-d563-c4ef7e18807d", + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4c2546e08a424179af511d8abe3c1c7d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "adapter_config.json: 0%| | 0.00/829 [00:00human\n", + "You are a function calling AI model. You are provided with function signatures within XML tags.You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.Here are the available tools: [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}] Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within XML tags as follows:\n", + "\n", + "{tool_call}\n", + "Also, before making a call to a function take the time to plan the function to take. Make that thinking process between {your thoughts}\n", + "\n", + "Hi, I need to convert 500 USD to Euros. Can you help me with that?\n", + "model\n", + "Alright, so I'm looking at this scenario where the user needs help converting 500 USD to Euros. Let's break it down step by step to understand how to approach this.\n", + "\n", + "First, the user has asked for conversion from USD to EUR. I know there's a specific tool available for currency conversion called `convert_currency`. This tool requires three parameters: amount, from_currency, and to_currency. In the user's message, they've provided all necessary details—500 USD and the desire to convert to Euros. \n", + "\n", + "I don't need to make any assumptions here because the user has already provided the exact amount and the target currency. My next step is to prepare the function call with the correct parameters. So, I'll structure the request with the amount as 500, from_currency as USD, and to_currency as EUR. \n", + "\n", + "This will be sent in JSON format as specified, ensuring that the tool receives all the necessary information to perform the conversion accurately. Once the function is called, it should return the converted amount, which I can then present back to the user.\n", + "\n", + "{'name': 'convert_currency', 'arguments': {'amount': 500, 'from_currency': 'USD', 'to_currency': 'EUR'}}\n", + "\n", + "tool\n", + "\n", + "{'converted_amount': 420.5, 'from_currency': 'USD', 'to_currency': 'EUR'}\n", + "\n", + "model\n", + "Sure, 500 USD is approximately 420.50 Euros.\n", + "human\n", + "That's great! Now, can you convert 1000 Euros back to USD?\n", + "model\n", + "Okay, so I'm trying to figure out how to handle the next function call based on the conversation and the previous steps. Let me see...\n", + "\n", + "In the conversation, the user first asked to convert 500 USD to Euros, and the model used the `convert_currency` function with the parameters amount=500, from_currency=USD, to_currency=EUR. Then, after getting the response, the model provided the converted amount. Now, the user is asking to convert 1000 Euros back to USD. \n", + "\n", + "So, the user's request is clear: convert 1000 EUR to USD. Since the function `convert_currency` is designed to handle currency conversions, and it requires amount, from_currency, and to_currency as parameters, it makes sense to call this function again with the updated values. \n", + "\n", + "The amount is 1000, from_currency should be EUR because the user is converting from Euros, and to_currency should be USD because they want the result in US Dollars. I don't need to make any assumptions here because all the required information is provided by the user. \n", + "\n", + "I'll structure the function call with these parameters. This should give the user the accurate converted amount from Euros to USD. Once the tool processes this, the model can present the result back to the user.\n", + "\n", + "{'name': 'convert_currency', 'arguments': {'amount': 1000, 'from_currency': 'EUR', 'to_currency': 'USD'}}\n", + "\n", + "tool\n", + "\n", + "{'converted_amount': 1190.0, 'from_currency': 'EUR', 'to_currency': 'USD'}\n", + "\n", + "model\n", + "1000 Euros is approximately 1190.00 USD.\n", + "human\n", + "Thank you for your help!\n", + "model\n", + "You're welcome! If you have any other questions, feel free to ask.\n", + "\n" + ] + } + ], + "source": [ + "print(dataset[\"test\"][8][\"text\"])" + ] + }, + { + "cell_type": "markdown", + "id": "b47fd511-ea00-47ce-8618-6e78e25672b2", + "metadata": { + "id": "b47fd511-ea00-47ce-8618-6e78e25672b2" + }, + "source": [ + "### Testing the model 🚀\n", + "\n", + "In that case, we will take the start of one of the samples from the test set and hope that it will generate the expected output.\n", + "\n", + "Since we want to test the function-calling capacities of our newly fine-tuned model, the input will be a user message with the available tools, a\n", + "\n", + "\n", + "### Disclaimer ⚠️\n", + "\n", + "The dataset we’re using **does not contain sufficient training data** and is purely for **educational purposes**. As a result, **your trained model’s outputs may differ** from the examples shown in this course. **Don’t be discouraged** if your results vary—our primary goal here is to illustrate the core concepts rather than produce a fully optimized or production-ready model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "37bf938d-08fa-4577-9966-0238339afcdb", + "metadata": { + "id": "37bf938d-08fa-4577-9966-0238339afcdb", + "outputId": "e97e7a1e-5ab2-46a2-dc3a-f436964fe004", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "human\n", + "You are a function calling AI model. You are provided with function signatures within XML tags.You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.Here are the available tools: [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}] Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within XML tags as follows:\n", + "\n", + "{tool_call}\n", + "Also, before making a call to a function take the time to plan the function to take. Make that thinking process between {your thoughts}\n", + "\n", + "Hi, I need to convert 500 USD to Euros. Can you help me with that?\n", + "model\n", + "Okay, so the user is asking to convert 500 USD to Euros. I need to figure out how to respond using the available functions. Let me look at the tools provided. There's a function called convert_currency which does exactly that—it converts one currency to another. The parameters required are amount, from_currency, and to_currency. \n", + "\n", + "The user provided the amount as 500, the source currency as USD, and the target currency as EUR. That fits perfectly with the function's parameters. I don't need to make any assumptions here because the user has given all the necessary details. \n", + "\n", + "So, I should call the convert_currency function with these arguments. That should give the user the converted amount they need.\n", + "\n", + "{'name': 'convert_currency', 'arguments': {'amount': 500, 'from_currency': 'USD', 'to_currency': 'EUR'}}\n", + "\n" + ] + } + ], + "source": [ + "#this prompt is a sub-sample of one of the test set examples. In this example we start the generation after the model generation starts.\n", + "prompt=\"\"\"human\n", + "You are a function calling AI model. You are provided with function signatures within XML tags.You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.Here are the available tools: [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}] Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within XML tags as follows:\n", + "\n", + "{tool_call}\n", + "Also, before making a call to a function take the time to plan the function to take. Make that thinking process between {your thoughts}\n", + "\n", + "Hi, I need to convert 500 USD to Euros. Can you help me with that?\n", + "model\n", + "\"\"\"\n", + "\n", + "inputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=False)\n", + "inputs = {k: v.to(\"cuda\") for k,v in inputs.items()}\n", + "outputs = model.generate(**inputs,\n", + " max_new_tokens=300,# Adapt as necessary\n", + " do_sample=True,\n", + " top_p=0.95,\n", + " temperature=0.01,\n", + " repetition_penalty=1.0,\n", + " eos_token_id=tokenizer.eos_token_id)\n", + "print(tokenizer.decode(outputs[0]))" + ] + }, + { + "cell_type": "markdown", + "id": "xWewPCZOyfJQ", + "metadata": { + "id": "xWewPCZOyfJQ" + }, + "source": [ + "## Congratulations\n", + "Congratulations on finishing this first Bonus Unit 🥳\n", + "\n", + "You've just **mastered what Function-Calling is and how to fine-tune your model to do Function-Calling**!\n", + "\n", + "If it's the first time you do this, it's normal that you're feeling puzzled. Take time to check the documentation and understand each part of the code and why we did it this way.\n", + "\n", + "Also, don't hesitate to try to **fine-tune different models**. The **best way to learn is by trying.**\n", + "\n", + "### Keep Learning, Stay Awesome 🤗" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "001b32600fdd418bb30c6b5ff85e269c": { + "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": 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