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HadeelHegazi/
ai_medical_dataset_train_epochs1max_steps60

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AI Medical Assistant Chatbot - Fine-tuning and Testing

Overview

This project involves fine-tuning a pretrained language model using the Unsloth framework for building an AI Medical Assistant Chatbot. The chatbot is designed to answer medical questions based on a specific dataset.

Setup

Install Required Packages

The following commands will install all necessary packages, including Unsloth, Xformers (Flash Attention), and other dependencies:

python

# Install Unsloth, Xformers (Flash Attention), and other packages
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes"
!pip install triton
!pip uninstall xformers
!pip install xformers

Import Modules

After installing the necessary packages, import the required modules:

python

from unsloth import FastLanguageModel
import torch

Model Loading and Configuration

Model Configuration

The model is configured with the following parameters:

max_seq_length: The maximum sequence length for the model (e.g., 2048).
dtype: Data type (e.g., None for auto-detection, float16 for certain GPUs).
load_in_4bit: Whether to use 4-bit quantization to reduce memory usage.

Pretrained Models

A list of pre-quantized 4-bit models supported by Unsloth is provided:

python

fourbit_models = [
    "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
    "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
    "unsloth/Meta-Llama-3.1-70B-bnb-4bit",
    "unsloth/Meta-Llama-3.1-405B-bnb-4bit",
    "unsloth/Mistral-Nemo-Base-2407-bnb-4bit",
    "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
    "unsloth/mistral-7b-v0.3-bnb-4bit",
    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
    "unsloth/Phi-3-mini-4k-instruct",
    "unsloth/Phi-3-medium-4k-instruct",
    "unsloth/gemma-2-9b-bnb-4bit",
    "unsloth/gemma-2-27b-bnb-4bit",
]

Loading and Configuring the Model

Load the model using the specified configuration:

python

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

Configure the model with PEFT (Parameter-Efficient Fine-Tuning):

python

model = FastLanguageModel.get_peft_model(
    model,
    r = 16,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_alpha = 16,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = False,
    loftq_config = None,
)

Dataset Preparation

Loading the Dataset

The medical dataset is loaded using the datasets library:

python

from datasets import load_dataset

dataset = load_dataset("ruslanmv/ai-medical-dataset")

if 'train' in dataset:
    dataset = dataset['train'].select(range(500))

Formatting the Dataset

The dataset is formatted to match the chatbot's expected input structure:

python

medical_prompt = """You are an AI Medical Assistant Chatbot, trained to answer medical questions. Below is an instruction that describes a task, paired with an response context. Write a response that appropriately completes the request.

### Question:
{}


### Context:
{}"""

EOS_TOKEN = tokenizer.eos_token

def formatting_prompts_func(examples):
    questions = examples["question"]
    contexts = examples["context"]
    texts = []
    for question, context in zip(questions, contexts):
        text = medical_prompt.format(question, context) + EOS_TOKEN
        texts.append(text)
    return {"text": texts}

dataset = dataset.map(formatting_prompts_func, batched=True)

Training

Trainer Configuration

Configure the trainer using the SFTTrainer and TrainingArguments:

python

from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    dataset_num_proc=2,
    packing=False,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        max_steps=60,
        learning_rate=2e-4,
        fp16=not is_bfloat16_supported(),
        bf16=is_bfloat16_supported(),
        logging_steps=1,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        output_dir="outputs",
    ),
)

Training Execution

Run the training:

trainer_stats = trainer.train()

Inference and Testing

Testing Before Training

To test the model before training, use the following script:

python

FastLanguageModel.for_inference(model)
inputs = tokenizer(
    [medical_prompt.format("What is the resurgent sodium current in mouse cerebellar Purkinje neurons?", "")],
    return_tensors="pt"
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=128)

Testing After Training

After training, test the model again to evaluate the performance:

python

if False:
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name="lora_model",
        max_seq_length=max_seq_length,
        dtype=dtype,
        load_in_4bit=load_in_4bit,
    )
    FastLanguageModel.for_inference(model)

inputs = tokenizer(
    [medical_prompt.format("What is the resurgent sodium current in mouse cerebellar Purkinje neurons?", "") + tokenizer.eos_token],
    return_tensors="pt"
).to("cuda")

model.eval()

_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=128)

Model Saving

Finally, save the trained model and tokenizer:

python

model.push_to_hub("HadeelHegazi/ai_medical_dataset_train_epochs1max_steps60", token="your_huggingface_token")
tokenizer.push_to_hub("HadeelHegazi/ai_medical_dataset_train_epochs1max_steps60", token="your_huggingface_token")

Issues and Debugging

If you encounter any issues, such as those depicted in the screenshot, ensure that you:

Check for Correct GPU Usage: Ensure your code is running on the correct GPU.
Clear CUDA Cache: Use torch.cuda.empty_cache() and gc.collect() to clear the cache.
Check Model and Tokenizer Configuration: Ensure all configurations are correct for the specific model.

base_model: unsloth/meta-llama-3.1-8b-bnb-4bit language:

en license: apache-2.0 tags:
text-generation-inference
transformers
unsloth
llama
trl

Uploaded model

Developed by: HadeelHegazi
License: apache-2.0
Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.