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

Libraries

How to use A-Aryam/AyurParam-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="A-Aryam/AyurParam-GGUF",
	filename="AyurParam_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use A-Aryam/AyurParam-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf A-Aryam/AyurParam-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf A-Aryam/AyurParam-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf A-Aryam/AyurParam-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf A-Aryam/AyurParam-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf A-Aryam/AyurParam-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf A-Aryam/AyurParam-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf A-Aryam/AyurParam-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf A-Aryam/AyurParam-GGUF:Q4_K_M

Use Docker

docker model run hf.co/A-Aryam/AyurParam-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use A-Aryam/AyurParam-GGUF with Ollama:
```
ollama run hf.co/A-Aryam/AyurParam-GGUF:Q4_K_M
```

Unsloth Studio

How to use A-Aryam/AyurParam-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for A-Aryam/AyurParam-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for A-Aryam/AyurParam-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for A-Aryam/AyurParam-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use A-Aryam/AyurParam-GGUF with Docker Model Runner:
```
docker model run hf.co/A-Aryam/AyurParam-GGUF:Q4_K_M
```

Lemonade

How to use A-Aryam/AyurParam-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull A-Aryam/AyurParam-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AyurParam-GGUF-Q4_K_M

List all available models

lemonade list

AyurParam GGUF

GGUF conversions of bharatgenai/AyurParam for local inference via Ollama and llama.cpp.

Converted by Aarya R. Thakar for use in ReassureAI — a hybrid AI healthcare assistant integrating modern biomedical and Ayurvedic guidance.

Available Variants

File	Quantization	Size	Quality	Recommended For
AyurParam-F16.gguf	F16	~5.34GB	Full precision	Evaluation, testing
AyurParam-Q4_K_M.gguf	Q4_K_M	~1.7GB	Good	✅ Production use

Usage

With Ollama

# Pull directly from HuggingFace
ollama run hf.co/A-Aryam/AyurParam-GGUF:Q4_K_M

# Or download and create manually
ollama create ayurparam -f Modelfile

Modelfile:

FROM ./AyurParam-Q4_K_M.gguf
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 50

With llama.cpp

./llama-cli -m AyurParam-Q4_K_M.gguf \
  -p "<user> What is Vata dosha? <assistant>" \
  --temp 0.6 --top-p 0.95

With LM Studio

Open LM Studio
Go to Search tab → search A-Aryam/AyurParam-GGUF
Download AyurParam-Q4_K_M.gguf
Load the model and start chatting

Prompt Format

<user> your question here <assistant>

Example:

<user> What is the Ayurvedic treatment for Amavata? <assistant>

Conversion Details

Converted: March 2025
Tool: llama.cpp (standard conversion pipeline)
Source: bharatgenai/AyurParam
Original license: CC-BY-4.0

Original Model

Model: bharatgenai/AyurParam
Organization: BharatGen AI
Base: Param-1-2.9B-Instruct → fine-tuned on Ayurvedic dataset
Paper: Coming soon (arxiv:2511.02374)

License

CC-BY-4.0 — same as the original model. Attribution: BharatGen AI / bharatgenai/AyurParam

Downloads last month: 664

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for A-Aryam/AyurParam-GGUF

Base model

bharatgenai/Param-1

Finetuned

bharatgenai/Param-1-2.9B-Instruct

Finetuned

bharatgenai/AyurParam

Quantized

(2)

this model

Paper for A-Aryam/AyurParam-GGUF

AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda

Paper • 2511.02374 • Published Nov 4, 2025 • 5