---
license: apache-2.0
base_model: ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025
tags:
  - medical
  - llama
  - biomedical
  - reasoning
  - lora
  - qlora
  - peft
pipeline_tag: text-generation
library_name: peft
language:
  - en
datasets:
  - openlifescienceai/medmcqa
---

# BioLLama LLM Adapters

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![PEFT](https://img.shields.io/badge/PEFT-LoRA-green)](https://github.com/huggingface/peft)
[![GitHub](https://img.shields.io/badge/GitHub-Source_Code-black)](https://github.com/jikaan/BioLLama-LLM)

## Model Description

**BioLLama LLM Adapters** are lightweight, parameter-efficient fine-tuning (PEFT) weights designed to enhance the clinical reasoning capabilities of the Llama-3 architecture.

These adapters were trained using **QLoRA** (Quantized Low-Rank Adaptation) on the **ContactDoctor Bio-Medical Llama-3.2-1B** base model. The primary objective of this fine-tuning is to improve Chain-of-Thought (CoT) generation for medical diagnostics and question answering, prioritizing logical step-by-step derivation over direct answer prediction.

## Technical Specifications

| Configuration | Details |
| :--- | :--- |
| **Base Model** | `ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025` |
| **Architecture** | Llama 3.2 (1B parameters) |
| **Adaptation Method** | LoRA (Low-Rank Adaptation) |
| **Quantization** | 4-bit (NF4) via `bitsandbytes` |
| **Target Modules** | Attention Projections (`q_proj`, `v_proj`) |
| **LoRA Rank (r)** | 16 |
| **LoRA Alpha** | 32 |
| **Training Epochs** | 3 |

## Performance and Evaluation

The model was evaluated on the **MedMCQA** validation set and a curated subset of **NEET PG 2024** (National Eligibility cum Entrance Test for Post-Graduation) clinical scenario questions.

| Metric | Score | Notes |
| :--- | :--- | :--- |
| **NEET PG Clinical Subset** | **72.7%** | Zero-shot accuracy on text-based clinical reasoning questions. |
| **Validation Accuracy** | **40.0%** | MedMCQA validation split. |
| **Inference Mode** | Greedy Decoding | Evaluated without sampling to ensure deterministic outputs. |

## Usage

### Prerequisites

To use these adapters, ensure `peft`, `transformers`, and `bitsandbytes` are installed.

```bash
pip install transformers peft torch bitsandbytes accelerate
```
Inference Pipeline

The following script demonstrates how to load the base model and apply the BioLLama adapters.
Python
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

BASE_MODEL_ID = "ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025"
ADAPTER_ID = "calender/BioLLama-LLM-Adapters"

def load_inference_model():
    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)

    base_model = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL_ID,
        device_map="auto",
        torch_dtype=torch.float16,
    )

    model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
    return model, tokenizer

model, tokenizer = load_inference_model()

query = "A 45-year-old presents with fatigue and low hemoglobin. Suggest initial line of management."

inputs = tokenizer(query, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs, 
    max_new_tokens=256, 
    temperature=0.1, 
    do_sample=False # Deterministic for medical queries
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Limitations and Disclaimer

    Research Use Only: This model is intended for academic research and development purposes. It is not a certified medical device.

    Clinical Decision Making: The outputs of this model should not be used for direct patient care, diagnosis, or treatment planning without verification by a qualified healthcare professional.

    Hallucinations: As with all Large Language Models, this model may generate plausible-sounding but factually incorrect medical information.

Citation

If you utilize this work, please cite it as follows:
```

@misc{calendar2025biollama,
  title = {BioLLama LLM Adapters: Fine-tuned Medical Reasoning System},
  author = {Calendar, S.},
  year = {2025},
  publisher = {Hugging Face},
  url = {[https://huggingface.co/calender/BioLLama-LLM-Adapters](https://huggingface.co/calender/BioLLama-LLM-Adapters)}
}