Instructions to use Experimentlab/Vigyan-Models-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Experimentlab/Vigyan-Models-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 Experimentlab/Vigyan-Models-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Experimentlab/Vigyan-Models-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 Experimentlab/Vigyan-Models-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Experimentlab/Vigyan-Models-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 Experimentlab/Vigyan-Models-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Experimentlab/Vigyan-Models-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 Experimentlab/Vigyan-Models-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Experimentlab/Vigyan-Models-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Experimentlab/Vigyan-Models-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Experimentlab/Vigyan-Models-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Experimentlab/Vigyan-Models-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Experimentlab/Vigyan-Models-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Experimentlab/Vigyan-Models-GGUF:Q4_K_M

Ollama
How to use Experimentlab/Vigyan-Models-GGUF with Ollama:
```
ollama run hf.co/Experimentlab/Vigyan-Models-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Experimentlab/Vigyan-Models-GGUF with Docker Model Runner:
```
docker model run hf.co/Experimentlab/Vigyan-Models-GGUF:Q4_K_M
```

Lemonade

How to use Experimentlab/Vigyan-Models-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Experimentlab/Vigyan-Models-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vigyan-Models-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

⚛ Vigyan AI: Sovereign Quantized GGUF Suite [Research Preview]

This repository contains official GGUF quantizations of the Vigyan AI STEM Model Series engineered for 100% offline, air-gapped on-device deployment across consumer hardware, laptops, and edge devices.

📦 Available Checkpoints & Quantizations

File	Quant Method	Target Hardware	Recommended Context
`vigyan-2b-reasoning-q4_k_m.gguf`	Q4_K_M (1.1 GB)	Mobile, WebGPU, 4GB RAM Laptops	4,096 tokens
`vigyan-2b-reasoning-q8_0.gguf`	Q8_0 (1.9 GB)	High-precision on-device 8GB Laptops	4,096 tokens

🚀 Quickstart via llama.cpp

llama-cli -m vigyan-2b-reasoning-q8_0.gguf -t 4 --temp 0.2 --repeat-penalty 1.15 -p "<|user|>\nExplain Ohm's Law and RC time constant.\n<|assistant|>\n"

🔬 Research & Experimental Notice

These checkpoints represent intermediate research iterations developed by Vigyan AI to evaluate sovereign neuro-symbolic SLM execution combined with deterministic symbolic solvers (SymPy) and embedded C++ GraphRAG.

Downloads last month: 35

GGUF

Model size

2B params

Architecture

olmo2

Hardware compatibility

4-bit

8-bit

Collection including Experimentlab/Vigyan-Models-GGUF

Sovereign STEM SLMs & MoE Architectures

Collection

Official flagship Sparse Mixture-of-Experts and ternary 1.58-bit SLMs engineered for offline STEM intelligence. • 11 items • Updated 1 day ago