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XinyuanWang/
qwen3-8b-medical-lora

Name: qwen3-8b-medical-lora
Availability: InStock

OpenMed/Medical-Reasoning-SFT-Mega

Qwen/Qwen3-8B

adapter

apache-2.0

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Medical LLM Fine-tuning & RAG Pipeline (Qwen3-8B)

Fine-tuning Qwen3-8B on medical domain data using QLoRA, with a Retrieval-Augmented Generation (RAG) system built on top for knowledge-grounded medical question answering.

Model on Hugging Face: XinyuanWang/qwen3-8b-medical-lora

Overview

This project consists of two main components:

QLoRA Fine-tuning — Qwen3-8B is fine-tuned on medical Q&A data using 4-bit NF4 quantization and LoRA adapters, reducing GPU memory requirements while preserving model quality.
RAG Pipeline — A FAISS-based retrieval system indexes 125,847 chunks from 18 classic medical textbooks (MedRAG/textbooks). At inference time, relevant passages are retrieved and injected into the prompt before generation.

Results

Metric	Value
Final training loss	0.8553
Final eval loss	0.9455
Train/eval gap	0.091 (~10%, no overfitting)
LoRA adapter size	174 MB (vs. 16 GB full model)
RAG knowledge base	125,847 chunks, 18 textbooks
Retrieval speed	~145 docs/sec (BGE-M3 on GPU)

Setup

Requirements

bash

# Fine-tuning
cd LLaMA-Factory && pip install -e ".[torch,bitsandbytes]"

# RAG + inference
pip install torch transformers peft bitsandbytes accelerate
pip install sentence-transformers faiss-cpu datasets

1. Prepare Training Data

bash

python scripts/download_dataset.py    # downloads 1,000 samples from OpenMed/Medical-Reasoning-SFT-Mega
python scripts/prepare_dataset.py     # converts to Alpaca format → data/medical_train.json

2. Fine-tune the Model

bash

python scripts/download_base_model.py                      # downloads Qwen3-8B locally
cp configs/dataset_info.json LLaMA-Factory/data/
cd LLaMA-Factory
llamafactory-cli train ../configs/train_medical_ft.yaml    # output: saves/qwen3-8b-med-lora/

3. Build the RAG Index

bash

python scripts/download_medrag_textbooks.py   # downloads MedRAG/textbooks → data/medrag_textbooks.jsonl
python scripts/build_rag_index.py             # builds FAISS index → rag_index/

4. Run Inference

bash

# Interactive mode
python scripts/rag_inference.py --interactive

# Single query
python scripts/rag_inference.py --query "What are the symptoms of appendicitis?"

# Batch inference (50 samples, saves to data/ragas_input.json)
python scripts/run_rag_inference.py

Model Details

Base model: Qwen/Qwen3-8B

Fine-tuning configuration:

Parameter	Value
Method	QLoRA (SFT)
Quantization	4-bit NF4 + double quantization
LoRA rank / alpha	16 / 32
LoRA dropout	0.05
Target modules	q/k/v/o_proj, gate/up/down_proj
Epochs	3
Effective batch size	8 (2 × 4 gradient accumulation)
Learning rate	5e-5 (cosine, 10% warmup)
Sequence length	512 tokens (packing enabled)
Precision	bfloat16
Framework	LLaMA-Factory

Training data: OpenMed/Medical-Reasoning-SFT-Mega — 1,000 samples, 4,966 Alpaca records after conversion.

RAG Details

Knowledge base: MedRAG/textbooks — 18 medical textbooks including Harrison's Internal Medicine, Schwartz's Surgery, Adams' Neurology, Katzung Pharmacology, Robbins Pathology, and more.

Pipeline:

Query → BGE-M3 Embedding → FAISS Top-5 Retrieval → Prompt → Qwen3-8B → Answer

Component	Choice
Embedding model	BAAI/bge-m3 (1024-dim)
Index type	`faiss.IndexFlatIP` (exact cosine search)
Retrieval threshold	0.45 cosine similarity
Generation	4-bit NF4, greedy decoding, `enable_thinking=False`

Repository Structure

medical-llm-finetune/
├── configs/
│   ├── train_medical_ft.yaml       # Training configuration
│   └── dataset_info.json           # LLaMA-Factory dataset registry
├── data/
│   ├── medical_reasoning_1k.json   # Raw downloaded samples
│   ├── medical_train.json          # Alpaca format training set
│   └── ragas_input.json            # Batch inference results (50 samples)
├── saves/
│   └── qwen3-8b-med-lora/          # LoRA adapter + tokenizer
├── rag_index/                      # FAISS index + chunk metadata (local only)
├── models/                         # Base model weights (local only)
└── scripts/
    ├── download_dataset.py
    ├── prepare_dataset.py
    ├── download_base_model.py
    ├── download_medrag_textbooks.py
    ├── build_rag_index.py
    ├── rag_inference.py
    └── run_rag_inference.py

XinyuanWang/qwen3-8b-medical-lora

Medical LLM Fine-tuning & RAG Pipeline (Qwen3-8B)

Overview

Results

Setup

Requirements

1. Prepare Training Data

2. Fine-tune the Model

3. Build the RAG Index

4. Run Inference

Model Details

RAG Details

Repository Structure

References

XinyuanWang/
qwen3-8b-medical-lora