Instructions to use udgamlabs/Gargi-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 udgamlabs/Gargi-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 udgamlabs/Gargi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf udgamlabs/Gargi-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 udgamlabs/Gargi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf udgamlabs/Gargi-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 udgamlabs/Gargi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf udgamlabs/Gargi-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 udgamlabs/Gargi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf udgamlabs/Gargi-GGUF:Q4_K_M

Use Docker

docker model run hf.co/udgamlabs/Gargi-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use udgamlabs/Gargi-GGUF with Ollama:
```
ollama run hf.co/udgamlabs/Gargi-GGUF:Q4_K_M
```
Unsloth Desktop

How to use udgamlabs/Gargi-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Gargi-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "udgamlabs/Gargi-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use udgamlabs/Gargi-GGUF with Docker Model Runner:
```
docker model run hf.co/udgamlabs/Gargi-GGUF:Q4_K_M
```

Lemonade

How to use udgamlabs/Gargi-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull udgamlabs/Gargi-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gargi-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use udgamlabs/Gargi-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Gargi-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default udgamlabs/Gargi-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use udgamlabs/Gargi-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Gargi-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "udgamlabs/Gargi-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

UDGAM LABS · Language model, GGUF files · 384M parameters

Gargi-GGUF

GGUF builds of Gargi, a 384M-parameter English model trained from scratch by Udgam Labs, for llama.cpp, Ollama, LM Studio and other GGUF runtimes. See the main model card for evaluation, safety and limitations.

File	Size	Notes
`Gargi-Q8_0.gguf`	410 MB	recommended
`Gargi-Q4_K_M.gguf`	238 MB	for tight memory
`Gargi-f16.gguf`	770 MB	reference precision

Run

ollama run hf.co/udgamlabs/Gargi-GGUF:Q8_0
llama-cli -hf udgamlabs/Gargi-GGUF:Q8_0 -c 16384

In Ollama, /set think turns on step-by-step thinking (/set nothink turns it off); Gargi writes thoughts mainly on maths problems. Keep the context at or below ~16K tokens for reliable recall. A ready-made Ollama Modelfile is included.

Quantization

GSM8K with thinking, first 200 test problems, greedy, via Ollama:

Build	Size	GSM8K (200)	Same answers as f16
f16	770 MB	39.0	–
Q8_0 (recommended)	410 MB	37.5	96.5%
Q4_K_M	238 MB	32.0	87%

Q8_0 matches f16 closely; Q4_K_M loses a few points (each score is ±3.4 points at this sample size). Use Q8_0 unless memory is tight.

Where it falls short

These files share Gargi's limits: see where it falls short on the main model card.

Licence

Apache-2.0.

About Udgam Labs. Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. udgamlabs.com

Downloads last month: 205

GGUF

Model size

0.4B params

Architecture

gemma4

Hardware compatibility

4-bit

8-bit

16-bit

Inference Providers NEW

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

Model tree for udgamlabs/Gargi-GGUF

Base model

udgamlabs/Gargi

Quantized

(3)

this model

Space using udgamlabs/Gargi-GGUF 1

Collection including udgamlabs/Gargi-GGUF

Gargi

Collection

384M English model trained from scratch on ~17B tokens. Thinks step by step, holds a conversation, reads ~16K tokens. Apache-2.0. • 5 items • Updated about 16 hours ago