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"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GLXwJqbjtPho"
},
"outputs": [],
"source": [
"!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import torch\n",
"from datasets import load_dataset\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" BitsAndBytesConfig,\n",
" HfArgumentParser,\n",
" TrainingArguments,\n",
" pipeline,\n",
" logging,\n",
")\n",
"from peft import LoraConfig, PeftModel #peft freezes most of the weights of llm model and only some of the weights are retrained using transfer learning\n",
"from trl import SFTTrainer"
],
"metadata": {
"id": "nAMzy_0FtaUZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#**In case of Llama 2, the following prompt template is used for the chat models**"
],
"metadata": {
"id": "J5d58CfLoWZj"
}
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "AS1Ee8JunXgp"
}
},
{
"cell_type": "markdown",
"source": [
"- Orignal Dataset: https://huggingface.co/datasets/timdettmers/openassistant-guanaco"
],
"metadata": {
"id": "YS0zP2DKpJRY"
}
},
{
"cell_type": "markdown",
"source": [
"- Reformat Dataset following the Llama 2 template with 1k sample: https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k"
],
"metadata": {
"id": "DKzi2s6RpNdH"
}
},
{
"cell_type": "markdown",
"source": [
"1. Load a llama-2-7b-chat-hf model (chat model)\n",
"2. Train it on the mlabonne/guanaco-llama2-1k (1,000 samples), which will produce our fine-tuned model Llama-2-7b-chat-finetune"
],
"metadata": {
"id": "A3XhjPfHq_mw"
}
},
{
"cell_type": "markdown",
"source": [
"QLoRA will use a rank of 64 with a scaling parameter of 16. We’ll load the Llama 2 model directly in 4-bit precision using the NF4 type and train it for one epoch"
],
"metadata": {
"id": "l6CPejEgv7hq"
}
},
{
"cell_type": "code",
"source": [
"# The model that you want to train from the Hugging Face hub\n",
"model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
"\n",
"# The instruction dataset to use\n",
"dataset_name = \"mlabonne/guanaco-llama2-1k\"\n",
"\n",
"# Fine-tuned model name\n",
"new_model = \"Llama-2-7b-chat-finetune\"\n",
"\n",
"\n",
"# QLoRA parameters\n",
"\n",
"\n",
"# LoRA attention dimension\n",
"lora_r = 64\n",
"\n",
"# Alpha parameter for LoRA scaling\n",
"lora_alpha = 16\n",
"\n",
"# Dropout probability for LoRA layers\n",
"lora_dropout = 0.1\n",
"\n",
"# bitsandbytes parameters\n",
"\n",
"\n",
"# Activate 4-bit precision base model loading\n",
"use_4bit = True\n",
"\n",
"# Compute dtype for 4-bit base models\n",
"bnb_4bit_compute_dtype = \"float16\"\n",
"\n",
"# Quantization type (fp4 or nf4)\n",
"bnb_4bit_quant_type = \"nf4\"\n",
"\n",
"# Activate nested quantization for 4-bit base models (double quantization)\n",
"use_nested_quant = False\n",
"\n",
"# TrainingArguments parameters\n",
"\n",
"\n",
"# Output directory where the model predictions and checkpoints will be stored\n",
"output_dir = \"./results\"\n",
"\n",
"# Number of training epochs\n",
"num_train_epochs = 1\n",
"\n",
"# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
"fp16 = False\n",
"bf16 = False\n",
"\n",
"# Batch size per GPU for training\n",
"per_device_train_batch_size = 4\n",
"\n",
"# Batch size per GPU for evaluation\n",
"per_device_eval_batch_size = 4\n",
"\n",
"# Number of update steps to accumulate the gradients for\n",
"gradient_accumulation_steps = 1\n",
"\n",
"# Enable gradient checkpointing\n",
"gradient_checkpointing = True\n",
"\n",
"# Maximum gradient normal (gradient clipping)\n",
"max_grad_norm = 0.3\n",
"\n",
"# Initial learning rate (AdamW optimizer)\n",
"learning_rate = 2e-4\n",
"\n",
"# Weight decay to apply to all layers except bias/LayerNorm weights\n",
"weight_decay = 0.001\n",
"\n",
"# Optimizer to use\n",
"optim = \"paged_adamw_32bit\"\n",
"\n",
"# Learning rate schedule\n",
"lr_scheduler_type = \"cosine\"\n",
"\n",
"# Number of training steps (overrides num_train_epochs)\n",
"max_steps = -1\n",
"\n",
"# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
"warmup_ratio = 0.03\n",
"\n",
"# Group sequences into batches with same length\n",
"# Saves memory and speeds up training considerably\n",
"group_by_length = True\n",
"\n",
"# Save checkpoint every X updates steps\n",
"save_steps = 0\n",
"\n",
"# Log every X updates steps\n",
"logging_steps = 25\n",
"\n",
"#\n",
"# SFT parameters\n",
"\n",
"# Maximum sequence length to use\n",
"max_seq_length = None\n",
"\n",
"# Pack multiple short examples in the same input sequence to increase efficiency\n",
"packing = False\n",
"\n",
"# Load the entire model on the GPU 0\n",
"device_map = {\"\": 0}"
],
"metadata": {
"id": "ib_We3NLtj2E"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#**Step 4:Load everything and start the fine-tuning process**"
],
"metadata": {
"id": "G9w5Txi5wI2B"
}
},
{
"cell_type": "code",
"source": [
"# Load dataset (you can process it here)\n",
"dataset = load_dataset(dataset_name, split=\"train\")\n",
"\n",
"# Load tokenizer and model with QLoRA configuration\n",
"compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=use_4bit,\n",
" bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
" bnb_4bit_compute_dtype=compute_dtype,\n",
" bnb_4bit_use_double_quant=use_nested_quant,\n",
")\n",
"\n",
"# Check GPU compatibility with bfloat16\n",
"if compute_dtype == torch.float16 and use_4bit:\n",
" major, _ = torch.cuda.get_device_capability()\n",
" if major >= 8:\n",
" print(\"=\" * 80)\n",
" print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
" print(\"=\" * 80)\n",
"\n",
"# Load base model\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" quantization_config=bnb_config,\n",
" device_map=device_map\n",
")\n",
"model.config.use_cache = False\n",
"model.config.pretraining_tp = 1\n",
"\n",
"# Load LLaMA tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
"\n",
"# Load LoRA configuration\n",
"peft_config = LoraConfig(\n",
" lora_alpha=lora_alpha,\n",
" lora_dropout=lora_dropout,\n",
" r=lora_r,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
")\n",
"\n",
"# Set training parameters\n",
"training_arguments = TrainingArguments(\n",
" output_dir=output_dir,\n",
" num_train_epochs=num_train_epochs,\n",
" per_device_train_batch_size=per_device_train_batch_size,\n",
" gradient_accumulation_steps=gradient_accumulation_steps,\n",
" optim=optim,\n",
" save_steps=save_steps,\n",
" logging_steps=logging_steps,\n",
" learning_rate=learning_rate,\n",
" weight_decay=weight_decay,\n",
" fp16=fp16,\n",
" bf16=bf16,\n",
" max_grad_norm=max_grad_norm,\n",
" max_steps=max_steps,\n",
" warmup_ratio=warmup_ratio,\n",
" group_by_length=group_by_length,\n",
" lr_scheduler_type=lr_scheduler_type,\n",
" report_to=\"tensorboard\"\n",
")\n",
"\n",
"# Set supervised fine-tuning parameters\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" train_dataset=dataset,\n",
" peft_config=peft_config,\n",
" dataset_text_field=\"text\",\n",
" max_seq_length=max_seq_length,\n",
" tokenizer=tokenizer,\n",
" args=training_arguments,\n",
" packing=packing,\n",
")\n",
"\n",
"# Train model\n",
"trainer.train()"
],
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{
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"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
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"Downloading readme: 0%| | 0.00/1.02k [00:00\n",
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" [171/250 20:31 < 09:35, 0.14 it/s, Epoch 0.68/1]\n",
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{
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"data": {
"text/plain": [
"TrainOutput(global_step=250, training_loss=1.3625773239135741, metrics={'train_runtime': 1686.0894, 'train_samples_per_second': 0.593, 'train_steps_per_second': 0.148, 'total_flos': 8755214190673920.0, 'train_loss': 1.3625773239135741, 'epoch': 1.0})"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"# Save trained model\n",
"trainer.model.save_pretrained(new_model)"
],
"metadata": {
"id": "aQNaFYp40M6k"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"##**Step 5: Check the plots on tensorboard, as follows**"
],
"metadata": {
"id": "NBtXo_Tgw43o"
}
},
{
"cell_type": "markdown",
"source": [
"###**Step 6:Use the text generation pipeline to ask questions like “What is a large language model?” Note that I’m formatting the input to match Llama 2 prompt template.**"
],
"metadata": {
"id": "TDqxnjvExGG6"
}
},
{
"cell_type": "code",
"source": [
"# Ignore warnings\n",
"logging.set_verbosity(logging.CRITICAL)\n",
"\n",
"# Run text generation pipeline with our next model\n",
"prompt = \"How can i help others?\"\n",
"pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
"result = pipe(f\"[INST] {prompt} [/INST]\")\n",
"print(result[0]['generated_text'])"
],
"metadata": {
"id": "frlSLPin4IJ4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0935cd2b-d172-452f-c486-307b0278765d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:90: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[INST] How can i help others? [/INST] There are many ways you can help others, depending on your interests and skills. Here are a few ideas:\n",
"\n",
"1. Volunteer: Look for volunteer opportunities in your community, such as helping at a local food bank, animal shelter, or nursing home.\n",
"2. Donate: Consider donating money or goods to organizations that help others, such as a homeless shelter or a disaster relief organization.\n",
"3. Mentor: If you have a particular skill or expertise, consider mentoring someone who could benefit from your knowledge.\n",
"4. Advocate: If you see an injustice or inequality, consider advocating for change. This could involve speaking out against discrimination, writing letters to your elected representatives, or participating in a protest or demonstration.\n",
"5. Educate yourself: Take the time to learn about the issues facing your\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#**Step 7: Store New Llama2 Model (Llama-2-7b-chat-finetune)**"
],
"metadata": {
"id": "CwbthYYhxs8p"
}
},
{
"cell_type": "code",
"source": [
"# Reload model in FP16 and merge it with LoRA weights\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" low_cpu_mem_usage=True,\n",
" return_dict=True,\n",
" torch_dtype=torch.float16,\n",
" device_map=device_map,\n",
")\n",
"model = PeftModel.from_pretrained(base_model, new_model)\n",
"model = model.merge_and_unload()\n",
"\n",
"# Reload tokenizer to save it\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"tokenizer.padding_side = \"right\""
],
"metadata": {
"id": "QQn30cRtAZ-P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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},
"outputId": "04299a41-810a-4573-d23d-615179ab6572"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00[INST] {prompt} [/INST]\")\n",
"print(result[0]['generated_text'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FaE8EJsfh8m6",
"outputId": "62d66fba-a98d-4b3c-eadd-c417fe642579"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
"pip install xformers.\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[INST] How can you study? [/INST] I can study by learning from a wide range of sources, including books, articles, and online resources. I can also learn from other people by asking questions and engaging in conversations. Additionally, I can use various tools and techniques to help me learn and retain information, such as note-taking, summarizing, and creating flashcards.\n",
"\n",
"However, I must note that I am not a human and do not have the same cognitive abilities as humans. I am a machine learning model that is trained on a large dataset of text, and I can only learn and understand the information that is available to me. I do not have personal experiences or emotions, and I do not have the ability to reason or make decisions in the same way that humans do.\n",
"\n",
"Overall, I am designed to assist and provide information to users, and I can do this by learning from a wide range of sources\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "rGHJvOhTiGuB"
},
"execution_count": null,
"outputs": []
}
]
}