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Model Details
Model Provider
xenkrypt
Model Tree
meta-llama/Llama-3.3-70B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Metric | Base Model | MedLlama-India-70B | Improvement |
|---|---|---|---|
| Win Rate (on dataset) | 20 | 80 | +300% relative |
| Medical Domain Win Rate | 26 | 74 | +185% relative |
| 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.
| 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 |
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.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport 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 feverB) Infective endocarditisC) CongenitalD) 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))https://huggingface.co/spaces/xenkrypt/MedLlama-India-Demo
Adaption Labs AutoScientist Hackathon — Healthcare, Part 1, July 2025
| Training platform | Adaption Labs AutoScientist |
| Data platform | Adaption Labs Adaptive Data |
| Training examples | 38,000+ |
| Source dataset | openlifescienceai/medmcqa |