Update README.md

README.md

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----
-license: other
-license_name: other
-license_link: LICENSE
----
+---
+language: en
+license: other
+library_name: peft
+pipeline_tag: text-generation
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+  - openlifescienceai/medmcqa
+tags:
+  - lora
+  - qlora
+  - peft
+  - unsloth
+  - medmcqa
+  - medical
+  - instruction-tuning
+  - llama
+metrics:
+  - accuracy
+---
+
+# MedMCQA LoRA — Meta-Llama-3-8B-Instruct
+
+**Adapter weights only** for `meta-llama/Meta-Llama-3-8B-Instruct`, fine-tuned to answer **medical multiple-choice questions (A/B/C/D)**.  
+Subjects used for fine-tuning and evaluation: **Biochemistry** and **Physiology**.  
+> Educational use only. Not medical advice.
+
+> **Access note:** Llama-3 base is a **public gated** model on HF.  
+> Accept the base model license on its page and use a **fine-grained token** that allows **public gated repos**.
+
+## Quick use (Transformers + PEFT)
+```python
+import os, re
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE = "meta-llama/Meta-Llama-3-8B-Instruct"
+ADAPTER = "Pk3112/medmcqa-lora-llama3-8b-instruct"
+hf_token = os.getenv("HUGGINGFACE_HUB_TOKEN")  # required if not logged in
+
+tok = AutoTokenizer.from_pretrained(BASE, use_fast=True, token=hf_token)
+base = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto", token=hf_token)
+model = PeftModel.from_pretrained(base, ADAPTER, token=hf_token).eval()
+
+prompt = (
+    "Question: Which vitamin is absorbed in the ileum?\n"
+    "A. Vitamin D\nB. Vitamin B12\nC. Iron\nD. Fat\n\n"
+    "Answer:"
+)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=8, do_sample=False)
+text = tok.decode(out[0], skip_special_tokens=True)
+
+m = re.search(r"Answer:\s*([A-D])\b", text)
+print(f"Answer: {m.group(1)}" if m else text.strip())
+```
+
+*Tip:* For rich explanations, increase `max_new_tokens`. For answer-only, keep it small and stop after the letter to reduce latency.
+
+## Results (Biochemistry + Physiology)
+| Model | Internal val acc (%) | Original val acc (%) | TTFT (ms) | Gen time (ms) | In/Out tokens |
+|---|---:|---:|---:|---:|---:|
+| **Llama-3-8B (LoRA)** | **83.83** | **65.20** | 567 | 14874 | 148 / 80 |
+
+## Training (summary)
+- Frameworks: **Unsloth + PEFT/LoRA** (QLoRA NF4)
+- LoRA: `r=32, alpha=64, dropout=0.0`; targets `q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj`
+- Max seq length: `768`
+- Objective: **answer-only** target (`Answer: <A/B/C/D>`)
+- Split: stratified **70/30** on `subject_name` (Biochemistry, Physiology)
+
+## Training code & reproducibility
+- **GitHub repo:** https://github.com/PranavKumarAV/MedMCQA-Chatbot-Finetune-Medical-AI
+- **Release (code snapshot):** https://github.com/PranavKumarAV/MedMCQA-Chatbot-Finetune-Medical-AI/releases/tag/v1.0-medmcqa
+
+## Files provided
+- `adapter_model.safetensors`
+- `adapter_config.json`
+
+## License & usage
+- **Adapter:** “Other” — adapter weights only; **use requires access to the base model** under the **Meta Llama 3 Community License** (accept on base model page)
+- **Base model:** `meta-llama/Meta-Llama-3-8B-Instruct` (public gated on HF)
+- **Dataset:** `openlifescienceai/medmcqa` — follow dataset license
+- **Safety:** Educational use only. Not medical advice.