README.md

---
language:
- en
- hi
- sa
license: llama3.2
library_name: transformers
tags:
- ayurveda
- medical
- biology
- llama-3.2
- text-generation
base_model: meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
model-index:
- name: VaidhLLaMA-3.2-3B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BhashaBench-Ayur
      type: evaluation-suite
    metrics:
    - name: Accuracy (Zero-Shot)
      type: accuracy
      value: 41.91
      verified: false
---

# VaidhLLaMA-3.2-3B-Instruct

**VaidhLLaMA-3.2-3B-Instruct** is a specialized Large Language Model fine-tuned for the domain of **Ayurveda**. It is built upon the Llama-3.2-3B-Instruct architecture and has been optimized to understand and reason with Ayurvedic concepts, physiology (*Sharir Kriya*), and clinical applications.

## Model Details

*   **Model Name:** VaidhLLaMA-3.2-3B-Instruct
*   **Base Model:** [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
*   **Developed By:** Vivekdas
*   **Language:** English, Hindi, Sanskrit (Domain-specific terminology)
*   **License:** Llama 3.2 Community License
*   **Architecture:** Transformer-based Auto-Regressive Language Model

## Performance

VaidhLLaMA demonstrates strong performance on the **BhashaBench-Ayur** benchmark, outperforming its base model and other similarly sized models in domain-specific tasks.

| Model | Accuracy (%) | Note |
| :--- | :--- | :--- |
| **VaidhLLaMA-3.2-3B** | **41.91%** | **Fine-tuned Ayurveda Specialist** |
| Llama-3.2-3B-Instruct | 40.74% | Base Model |
| Llama-3.2-1B | 27.58% | Tiny Model |

## Intended Use

This model is designed for:
*   Answering questions related to Ayurvedic medical science.
*   Explaining concepts from classical Ayurvedic texts (*Samhitas*).
*   Assisting researchers and students in the field of Ayurveda.

**Disclaimer:** This model is for **educational and research purposes only**. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.

## Usage

You can run this model using the `transformers` library:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Vivekdas/VaidhLLaMA-3.2-3B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are VaidhLLaMA, an expert AI assistant for Ayurveda."},
    {"role": "user", "content": "Explain the concept of Tridosha in Ayurveda."}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.6,
    top_p=0.9
)

response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{vaidhllama2024,
  author = {Vivekdas},
  title = {VaidhLLaMA: A Fine-Tuned LLM for Ayurveda},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/Vivekdas/VaidhLLaMA-3.2-3B-Instruct}}
}
```