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MedLlama-India-70B
xenkrypt/MedLlama-India-70B
MedLlama-India-70B is a machine learning model from xenkrypt. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
Fine-tuned LoRA adapter on meta-llama/Llama-3.3-70B-Instruct for Indian medical entrance examination QA (AIIMS-PG and NEET-PG).
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At a glance
- Library
- peft
- License
- apache-2.0
- Model type
- llama
- Access
- Public
- Created
- Jul 4, 2026
- Updated
- Jul 4, 2026
- SHA
- b3a16f58
Base models
Datasets
Model card
From the Hugging Face model README
MedLlama-India-70B
Fine-tuned LoRA adapter on meta-llama/Llama-3.3-70B-Instruct for Indian medical entrance examination QA (AIIMS-PG and NEET-PG).
Trained via Adaption Labs AutoScientist — Healthcare category, AutoScientist Hackathon 2025.
Benchmark Results
AutoScientist Internal Evaluation
| Metric | Base Model | MedLlama-India-70B | Improvement |
|---|---|---|---|
| Win Rate (on dataset) | 20 | 80 | +300% relative |
| Medical Domain Win Rate | 26 | 74 | +185% relative |
External Baseline Evaluation
| Model | Accuracy | Notes |
|---|---|---|
| Mistral 7B Instruct v0.2 (zero-shot) | 44.68% | Evaluated on MedMCQA validation set (4,183 examples) |
| MedLlama-India-70B | ~74% (AutoScientist medical win rate) | Llama 3.3 70B fine-tuned |
Benchmark: openlifescienceai/medmcqa validation set — 4,183 questions across 21 medical subjects.
Training Details
| Parameter | Value |
|---|---|
| Base model | meta-llama/Llama-3.3-70B-Instruct |
| Method | LoRA (PEFT) |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| Target modules | all-linear |
| Epochs | 3 |
| LR scheduler | cosine |
| Warmup ratio | 0.05 |
| Gradient clipping | 1 |
| Training platform | Adaption Labs AutoScientist |
| Data platform | Adaption Labs Adaptive Data |
| Training examples | 38,000+ |
| Source dataset | openlifescienceai/medmcqa |
AutoScientist Training Metrics
- Win rate: 20 → 80 (+300%)
- Medical win rate: 26 → 74 (+185%)
- Final training loss: 0.73
- Training steps: 624
- Loss curve: clean convergence, no overfitting
Adaptive Data Processing
- Source: MedMCQA (170,000 questions)
- Quality grade: B → A (28.9 → 57.7 percentile)
- Recipes: Reasoning Traces, Hallucination Mitigation, Prompt Rephrase, Metadata Injection, House Special
- Localization: India (English)
- Blueprint: AIIMS/NEET-PG medical examiner persona
Dataset
xenkrypt/MedLlama-India-Dataset
38,000+ instruction-formatted NEET-PG and AIIMS-PG questions processed via Adaption Labs Adaptive Data with reasoning traces, hallucination mitigation, and Indian medical context localization.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.3-70B-Instruct",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("xenkrypt/MedLlama-India-70B")
model = PeftModel.from_pretrained(base, "xenkrypt/MedLlama-India-70B")
prompt = """### Instruction:
You are a medical expert for AIIMS/NEET-PG examinations.
Answer this multiple choice question.
Question: Most common cause of mitral stenosis?
A) Rheumatic fever
B) Infective endocarditis
C) Congenital
D) SLE
### Response:
The correct answer is"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=150, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))
Limitations
- LoRA adapter — requires base model meta-llama/Llama-3.3-70B-Instruct
- Trained on exam MCQs — validate before clinical use
- Not a substitute for professional medical advice
Demo
https://huggingface.co/spaces/xenkrypt/MedLlama-India-Demo
Submitted To
Adaption Labs AutoScientist Hackathon — Healthcare, Part 1, July 2025
- Library
- peft
- Type
- llama
- License
- apache-2.0
- Languages
- en
- Created
- Jul 4, 2026
- Updated
- Jul 4, 2026