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"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n",
"Collecting datasets\n",
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"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
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]
}
],
"source": [
"# Install the requirements in Google Colab\n",
"!pip install transformers datasets trl huggingface_hub"
]
},
{
"cell_type": "code",
"source": [
"# Import necessary libraries\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"from datasets import load_dataset\n",
"from trl import SFTConfig, SFTTrainer, setup_chat_format, DataCollatorForCompletionOnlyLM\n",
"import torch"
],
"metadata": {
"id": "7iQRJ-YHDCQu"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device = (\n",
" \"cuda\"\n",
" if torch.cuda.is_available()\n",
" else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
")"
],
"metadata": {
"id": "VYETUQkNDIPz"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load the model and tokenizer\n",
"model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" pretrained_model_name_or_path=model_name\n",
").to(device)\n",
"tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)"
],
"metadata": {
"id": "olO-YsF-DMSh"
},
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Set up the chat format\n",
"model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
],
"metadata": {
"id": "L2H0JHzBDTpm"
},
"execution_count": 38,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Set our name for the finetune to be saved &/ uploaded to\n",
"finetune_name = \"SmolLM2-135M-SFT-smoltalk\"\n",
"finetune_tags = [\"smol-course\",\"sft_finetuning\"]"
],
"metadata": {
"id": "YBiQ7YZPDhKe"
},
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Let's test the base model before training\n",
"prompt = \"Write a haiku about programming\"\n",
"\n",
"# Format with template\n",
"messages = [{\"role\": \"user\", \"content\": prompt}]\n",
"formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
"\n",
"# Generate response\n",
"inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
"outputs = model.generate(**inputs, max_new_tokens=100)\n",
"print(\"Before training:\")\n",
"print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "av7kKBd0DhkZ",
"outputId": "27d49f1f-6f0b-4e77-d276-b5b482b8bfff"
},
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Before training:\n",
"user\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a haiku about programming\n",
"Write a\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Load a sample dataset\n",
"from datasets import load_dataset\n",
"ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
"ds"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qyWuLJDjDmqK",
"outputId": "0162aeae-94bf-4d73-ec2a-e53c35d96bb3"
},
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['full_topic', 'messages'],\n",
" num_rows: 2260\n",
" })\n",
" test: Dataset({\n",
" features: ['full_topic', 'messages'],\n",
" num_rows: 119\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"ds['train'][0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_6D93RBmDxbk",
"outputId": "1540bcb9-8191-44cd-b2ee-51abb6d0807a"
},
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'full_topic': 'Travel/Vacation destinations/Beach resorts',\n",
" 'messages': [{'content': 'Hi there', 'role': 'user'},\n",
" {'content': 'Hello! How can I help you today?', 'role': 'assistant'},\n",
" {'content': \"I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?\",\n",
" 'role': 'user'},\n",
" {'content': \"Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.\",\n",
" 'role': 'assistant'},\n",
" {'content': 'That sounds great. Are there any resorts in the Caribbean that are good for families?',\n",
" 'role': 'user'},\n",
" {'content': 'Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.',\n",
" 'role': 'assistant'},\n",
" {'content': \"Okay, I'll look into those. Thanks for the recommendations!\",\n",
" 'role': 'user'},\n",
" {'content': \"You're welcome. I hope you find the perfect resort for your vacation.\",\n",
" 'role': 'assistant'}]}"
]
},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [
"def process_messages(samples):\n",
" # Add 'human' role logic\n",
" result = []\n",
" for x in samples['messages']:\n",
" if x[-1]['role'] == 'user': # Add condition for 'human' role\n",
" result.append(x)\n",
" else:\n",
" result.append(x[:-1]) # Truncate the message if condition is not met\n",
" return {'messages': result}\n",
"\n",
"# Applying the function on a dataset\n",
"dataset = ds.map(process_messages, batched=True)"
],
"metadata": {
"id": "cSYoD4Y3FQdu"
},
"execution_count": 43,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Configure the SFTTrainer\n",
"sft_config = SFTConfig(\n",
" output_dir=\"./sft_output\",\n",
" max_steps=500, # Adjust based on dataset size and desired training duration\n",
" per_device_train_batch_size=16, # Set according to your GPU memory capacity\n",
" learning_rate=5e-5, # Common starting point for fine-tuning\n",
" logging_steps=50, # Frequency for finding training metrics\n",
" save_steps=50, # Frequency for saving model checkpoints\n",
" eval_strategy=\"steps\", # Evaluate the model at regular intervals\n",
" eval_steps=50, # Frequency of evaluation\n",
" use_mps_device=(\n",
" True if device == \"mps\" else False\n",
" ), # Use MPS for mixed precision training\n",
" hub_model_id=finetune_name, # Set a unique name for your model\n",
" report_to=[]\n",
")\n",
"\n",
"# Initialize the SFTTrainer\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" args=sft_config,\n",
" train_dataset=ds[\"train\"],\n",
" processing_class=tokenizer,\n",
" eval_dataset=ds[\"test\"],\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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]
},
"id": "nvtJ2H41JTrU",
"outputId": "0a9eaf66-f20d-4a44-d54f-6f6428cab4f1"
},
"execution_count": 44,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/119 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
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}
]
},
{
"cell_type": "code",
"source": [
"# Train the model\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 441
},
"id": "qF2OxgBoJXKO",
"outputId": "5a505e90-7213-4a80-a0b9-54338b83eadf"
},
"execution_count": 45,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [500/500 17:08, Epoch 3/4]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.250500</td>\n",
" <td>1.109389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>1.065500</td>\n",
" <td>1.067429</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>1.015600</td>\n",
" <td>1.044449</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.895800</td>\n",
" <td>1.034744</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>0.881400</td>\n",
" <td>1.030149</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>0.862100</td>\n",
" <td>1.029914</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>0.788500</td>\n",
" <td>1.028845</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>0.789500</td>\n",
" <td>1.027438</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>0.767000</td>\n",
" <td>1.030825</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.741700</td>\n",
" <td>1.031717</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=500, training_loss=0.9057471542358398, metrics={'train_runtime': 1030.1888, 'train_samples_per_second': 7.766, 'train_steps_per_second': 0.485, 'total_flos': 1302438402256896.0, 'train_loss': 0.9057471542358398, 'epoch': 3.52112676056338})"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"# Save the model\n",
"trainer.save_model(f\"./{finetune_name}\")"
],
"metadata": {
"id": "NJAdU1QBJfXK"
},
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"source": [
"trainer.push_to_hub(tags=finetune_tags)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 200,
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{
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"CommitInfo(commit_url='https://huggingface.co/ParitKansal/SmolLM2-135M-SFT-smoltalk/commit/97e8fed11e0a365f181dc40fc9b8ab4a87a98e99', commit_message='End of training', commit_description='', oid='97e8fed11e0a365f181dc40fc9b8ab4a87a98e99', pr_url=None, repo_url=RepoUrl('https://huggingface.co/ParitKansal/SmolLM2-135M-SFT-smoltalk', endpoint='https://huggingface.co', repo_type='model', repo_id='ParitKansal/SmolLM2-135M-SFT-smoltalk'), pr_revision=None, pr_num=None)"
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{
"cell_type": "code",
"source": [
"# Test the fine-tuned model on the same prompt\n",
"\n",
"# Let's test the base model before training\n",
"prompt = \"Write about a programming lang\"\n",
"\n",
"# Format with template\n",
"messages = [{\"role\": \"user\", \"content\": prompt}]\n",
"formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
"\n",
"# Generate response\n",
"inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
"\n",
"# TODO: use the fine-tuned to model generate a response, just like with the base example.\n",
"outputs = model.generate(**inputs, max_new_tokens=100)\n",
"print(\"After training:\")\n",
"print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7xNFWx2HPsK7",
"outputId": "0e29193f-be97-48bd-ca71-28be0c7b10e4"
},
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"After training:\n",
"user\n",
"Write about a programming lang\n",
"\n",
"What is a programming language?\n",
"\n",
"A programming language is a set of instructions that a computer can understand and execute. It is a set of rules that tells the computer what to do. It is a language that is easy to learn and use.\n",
"\n",
"What is a programming language used for?\n",
"\n",
"A programming language is used to create software programs. It is a language that is used to create computer programs. It is a language that is easy to learn and use.\n",
"\n",
"What\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "DEBtbcL_Vc88"
},
"execution_count": null,
"outputs": []
}
]
}