Skill: Low-Resource AI Researcher

ID: 215
Category: AI/ML Research
Language: Python
Framework: PyTorch + PEFT (LoRA/QLoRA) + Transformers

Overview

Based on Parameter-Efficient Fine-Tuning (PEFT) technology, trains high-performance medical domain large language models on consumer-grade GPUs or single A100. Supports advanced fine-tuning methods such as LoRA, QLoRA, optimized for medical text understanding and generation tasks.

Features

• 🚀 Parameter-Efficient Fine-Tuning: LoRA, QLoRA, DoRA support
• 🏥 Medical Domain Optimized: Pre-configured for medical QA, diagnosis, clinical notes
• 💻 Low-Resource Ready: Optimized for consumer GPUs (RTX 3090/4090) and single A100
• 📊 Quantization: 4-bit/8-bit quantization with bitsandbytes
• 🔄 Multi-Task: Supports SFT, DPO, and medical instruction tuning
• 📝 Medical Datasets: Built-in support for PubMedQA, MedQA, MIMIC-III

Installation

# Core dependencies
pip install torch transformers datasets accelerate peft bitsandbytes

# Optional for training optimization
pip install flash-attn --no-build-isolation
pip install wandb tensorboard

# Medical NLP utilities
pip install scispacy scikit-learn

Quick Start

from skills.low_resource_ai_researcher.scripts.main import MedicalPEFTTrainer

# Initialize trainer
trainer = MedicalPEFTTrainer(
    model_name="meta-llama/Llama-2-7b-hf",
    task="medical_qa"
)

# Train with LoRA
trainer.train(
    output_dir="./medical_lora_model",
    num_epochs=3,
    batch_size=4,
    use_qlora=True  # 4-bit quantization
)

Configuration

Hardware Profiles

LoRA Config

lora:
  r: 64              # LoRA rank
  lora_alpha: 128    # Scaling factor
  target_modules:    # Modules to apply LoRA
    - q_proj
    - v_proj
    - k_proj
    - o_proj
    - gate_proj
    - up_proj
    - down_proj
  lora_dropout: 0.05
  bias: "none"
  task_type: "CAUSAL_LM"

CLI Usage

# Basic training
python scripts/main.py \
    --model_name_or_path meta-llama/Llama-2-7b-hf \
    --dataset medical_qa \
    --output_dir ./output \
    --use_qlora \
    --per_device_train_batch_size 4

# With custom config
python scripts/main.py --config configs/medical_qlora.yaml

# Resume training
python scripts/main.py --resume_from_checkpoint ./output/checkpoint-1000

API Reference

MedicalPEFTTrainer