Instructions to use Vivekdas/VaidhLLaMA-3.2-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vivekdas/VaidhLLaMA-3.2-3B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Vivekdas/VaidhLLaMA-3.2-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vivekdas/VaidhLLaMA-3.2-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Vivekdas/VaidhLLaMA-3.2-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vivekdas/VaidhLLaMA-3.2-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vivekdas/VaidhLLaMA-3.2-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vivekdas/VaidhLLaMA-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vivekdas/VaidhLLaMA-3.2-3B-Instruct

SGLang

How to use Vivekdas/VaidhLLaMA-3.2-3B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vivekdas/VaidhLLaMA-3.2-3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vivekdas/VaidhLLaMA-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vivekdas/VaidhLLaMA-3.2-3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vivekdas/VaidhLLaMA-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vivekdas/VaidhLLaMA-3.2-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/Vivekdas/VaidhLLaMA-3.2-3B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
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:

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:

@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}}
}

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Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for Vivekdas/VaidhLLaMA-3.2-3B-Instruct

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

(1674)

this model

Quantizations

1 model

Evaluation results

Accuracy (Zero-Shot) on BhashaBench-Ayur
self-reported

41.910