Instructions to use bharatgenai/AyurParam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bharatgenai/AyurParam with Transformers:

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

pipe = pipeline("text-generation", model="bharatgenai/AyurParam", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bharatgenai/AyurParam", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bharatgenai/AyurParam with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bharatgenai/AyurParam

SGLang

How to use bharatgenai/AyurParam 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 "bharatgenai/AyurParam" \
    --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": "bharatgenai/AyurParam",
		"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 "bharatgenai/AyurParam" \
        --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": "bharatgenai/AyurParam",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bharatgenai/AyurParam with Docker Model Runner:
```
docker model run hf.co/bharatgenai/AyurParam
```

GGUF Conversion - Publishing F16 and Q4_K_M variants + Local Inference Query

by A-Aryam - opened Jun 6

Discussion

A-Aryam

Jun 6

I am integrating AyurParam into my healthcare AI project (ReassureAI) - a hybrid AI system combining modern biomedical and Ayurvedic guidance for Indian users, developed as my final year B.Tech project. Currently under active development.

Back in Feb-March 2026, I explored this repo and I converted the model to GGUF format using llama.cpp, creating F16 and Q4_K_M variants for local inference via Ollama.

I would like to publish these conversions on HuggingFace with full attribution to bharatgenai/AyurParam under CC-BY-4.0. Happy to include any specific attribution text you prefer.

Also wanted to confirm — is local inference via Ollama/llama.cpp the recommended approach, or do you have other suggestions?

Thank you for the great work on AyurParam.

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