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Train high-performance medical LLMs on consumer GPUs using parameter-efficient fine-tuning
---
name: low-resource-ai-researcher
description: Train high-performance medical LLMs on consumer GPUs using parameter-efficient
fine-tuning
version: 1.0.0
category: Research
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: Medium
skill_type: Tool/Script
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'
---
# 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
```bash
# 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
```python
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
| Profile | GPU Memory | Quantization | Max Model Size | Batch Size |
|---------|-----------|--------------|----------------|------------|
| consumer-24g | 24GB (RTX 3090/4090) | QLoRA 4-bit | 70B | 1-2 |
| a100-40g | 40GB (A100) | LoRA 8-bit | 70B | 4-8 |
| a100-80g | 80GB (A100) | LoRA 16-bit | 70B | 8-16 |
| multi-gpu | 2x A100 | LoRA 16-bit | 70B+ | 16+ |
### LoRA Config
```yaml
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
```bash
# 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
```python
trainer = MedicalPEFTTrainer(
model_name: str, # Base model name/path
task: str, # Task type: medical_qa, diagnosis, clinical_note
lora_r: int = 64, # LoRA rank
lora_alpha: int = 128, # LoRA alpha
use_qlora: bool = False, # Use 4-bit quantization
target_modules: List[str] = None,
device_map: str = "auto",
trust_remote_code: bool = True
)
```
### Methods
| Method | Description |
|--------|-------------|
| `train()` | Start fine-tuning with configured parameters |
| `evaluate()` | Evaluate on medical benchmark datasets |
| `merge_and_save()` | Merge LoRA weights and save full model |
| `load_model()` | Load a trained model for inference |
| `generate()` | Generate medical text/responses |
## Supported Models
- **LLaMA 2/3** (7B, 13B, 70B)
- **Mistral** (7B, 8x7B)
- **Yi** (6B, 34B)
- **Qwen** (7B, 14B, 72B)
- **Baichuan** (7B, 13B)
- **ChatGLM** (6B)
## Medical Datasets
| Dataset | Description | Size |
|---------|-------------|------|
| PubMedQA | Biomedical QA | 1k QA pairs |
| MedQA | USMLE-style questions | 61k |
| MedMCQA | Medical entrance exam QA | 194k |
| MIMIC-III | Clinical notes | De-identified |
| CMeEE | Chinese medical NER | 15k |
| Huatuo-26M | Chinese medical corpus | 26M samples |
## Performance Benchmarks
| Model | Method | GPU | Training Time | MedQA Acc |
|-------|--------|-----|---------------|-----------|
| LLaMA-2-7B | LoRA | A100-40G | 2h | 58.2% |
| LLaMA-2-7B | QLoRA | RTX 4090 | 3h | 57.8% |
| LLaMA-2-13B | QLoRA | A100-40G | 4h | 62.5% |
| Mistral-7B | LoRA | A100-40G | 2.5h | 61.3% |
## Best Practices
1. **Gradient Accumulation**: Use for effective larger batch sizes
2. **Learning Rate**: Start with 2e-4 for LoRA, 1e-4 for full fine-tuning
3. **Warmup Steps**: 100 steps for medical domain adaptation
4. **Max Length**: 2048-4096 for clinical notes, 512-1024 for QA
5. **Data Quality**: Filter out low-quality medical data carefully
## Troubleshooting
### Out of Memory
```python
# Enable gradient checkpointing
trainer.train(gradient_checkpointing=True)
# Reduce sequence length
trainer.train(max_seq_length=1024)
# Use DeepSpeed ZeRO-3 for large models
```
### Slow Training
```python
# Enable Flash Attention
trainer.train(use_flash_attention=True)
# Use bf16 on Ampere GPUs
trainer.train(bf16=True)
```
## License
This skill follows the license of the underlying models used. Medical applications require compliance with HIPAA/GDPR regulations.
## References
1. Hu et al. (2021) - LoRA: Low-Rank Adaptation of Large Language Models
2. Dettmers et al. (2023) - QLoRA: Efficient Finetuning of Quantized LLMs
3. Singhal et al. (2023) - Large Language Models Encode Clinical Knowledge
## Risk Assessment
| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
## Security Checklist
- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] Input file paths validated (no ../ traversal)
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no stack traces exposed)
- [ ] Dependencies audited
## Prerequisites
```bash
# Python dependencies
pip install -r requirements.txt
```
## Evaluation Criteria
### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable
### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time
## Lifecycle Status
- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**:
- Performance optimization
- Additional feature support
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