mistral-7b-medical-medqa-qlora

Mistral-7B-Instruct-v0.2 — Medical MedQA QLoRA Adapter (Partial-Epoch Checkpoint)

LoRA adapter fine-tuned on medalpaca/medical_meadow_medqa (USMLE-style 4-option clinical MCQs).

Honest status: this is a partial-training checkpoint, not the planned full run. Training was interrupted at ~step 171 of 1144 (~0.3 of 2 planned epochs) because the Kaggle 2× T4 model-parallel setup ran at ~2.5 min/step instead of the expected ~5 sec/step. A full-epoch re-run is planned on a single-GPU RunPod instance using train_runpod_medical.py.

Results (held-out test split)

Confidence interval at n=100: ±5pp (95% CI). True accuracy is plausibly in [34%, 44%].

What worked:

• Format adherence is solid — 0% no-answer rate. The model reliably starts responses with "The correct answer is X)".
• Clear lift above chance even with ~15% of planned training compute.

What didn't:

• Accuracy below the +5pp-vs-base target. Primary cause: undertraining.
• Test set includes some 5-option (A–E) samples the training filter missed (regex caught E) but not E:); the model only saw 4-option questions in training, so 5-option samples are harder.

Training details

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

BASE = "mistralai/Mistral-7B-Instruct-v0.2"
ADAPTER = "anksriv/mistral-7b-medical-medqa-qlora"  # this repo

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(model, ADAPTER)
tokenizer = AutoTokenizer.from_pretrained(BASE)

system = ("You are a knowledgeable medical AI assistant. "
          "When given a clinical multiple-choice question, analyze the case carefully, "
          "identify the correct answer (A, B, C, or D), and provide a clear explanation. "
          "Always begin your response with 'The correct answer is X)' where X is the letter.")

messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "A 32-year-old woman presents with ... A) ... B) ... C) ... D) ..."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
out = model.generate(inputs, max_new_tokens=200, temperature=0.1, do_sample=True)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Intended use & limitations

This is a research/educational artifact, not a clinical tool. Do not use it for medical decision-making. The 39% accuracy on USMLE-style questions, combined with partial training, makes this strictly inappropriate for any patient-facing or diagnostic application.

The adapter inherits all biases of:

• The base Mistral-7B-Instruct-v0.2 model
• The MedQA dataset (USMLE-skewed, US-centric, English-only)

Reproducibility

• Code: https://github.com/ankitsriv89/03-llm-finetuning
• Training notebook: notebooks/02_medical.ipynb
• Single-GPU RunPod script: scripts/train_runpod_medical.py
• Eval script: scripts/evaluate.py --mode mcq
• Seed: 42 (train/test split)

Planned full-run improvements

1. Run the full 2 epochs (~1144 steps) on a single A40/L40S/4090 (~30-45 min target).
2. Tighten the 5-option filter (catch both E) and E:).
3. Evaluate on the full 1K held-out set instead of 100.
4. Compare against base Mistral-7B-Instruct-v0.2 (no adapter) on the same eval set to compute the actual delta.