Instructions to use NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/G9v3-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/G9v3-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/G9v3-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/G9v3-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/G9v3-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/G9v3-3B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/G9v3-3B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/G9v3-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/G9v3-3B-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": "NANI-Nithin/G9v3-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/G9v3-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/G9v3-3B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/G9v3-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/G9v3-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.G9v3-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-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 NANI-Nithin/G9v3-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/G9v3-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/G9v3-3B-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 "NANI-Nithin/G9v3-3B-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"

G9v3-3B-GGUF

GGUF quantized releases of ai9stars/G9v3-3B for llama.cpp and compatible runtimes.

Model Information

Base Model: ai9stars/G9v3-3B
Parameter Size: 3B
Format: GGUF
Quantized By: NANI-Nithin
Quantization Tool: llama.cpp

Available Files

2-bit

Q2_K
IQ2_M
Q2_K_L

3-bit

IQ3_XXS
IQ3_XS
Q3_K_S
IQ3_M
Q3_K_M
Q3_K_L
Q3_K_XL

4-bit

IQ4_XS
IQ4_NL
Q4_0
Q4_1
Q4_K_S
Q4_K_M

5-bit

Q5_K_S
Q5_K_M

6-bit

Q6_K
Q6_K_L

8-bit

Q8_0

Full Precision

F16/BF16 GGUF

Recommended Quantizations

Best Overall

Q4_K_M

Recommended for most users. Excellent balance of quality, memory usage, and speed.

Higher Quality

Q5_K_M or Q6_K

For users seeking maximum quality while still benefiting from quantization.

Best IQ Quant

IQ4_NL

Excellent quality-per-GB and one of the strongest modern importance-aware quantizations.

Low Memory Systems

IQ3_M or Q3_K_M

Good balance of usability and reduced memory requirements.

IQ Quantizations

The following importance-aware quantizations are included:

IQ2_M
IQ3_XXS
IQ3_XS
IQ3_M
IQ4_XS
IQ4_NL

These quantizations were generated using an importance matrix (imatrix) calibration pass and typically provide improved quality retention compared to traditional quantization methods at similar file sizes.

Usage

llama.cpp

./llama-cli \
  -m G9v3-3B-Q4_K_M.gguf \
  -p "Hello!"

Ollama

Create a Modelfile:

FROM ./G9v3-3B-Q4_K_M.gguf

Then:

ollama create g9v3-3b -f Modelfile
ollama run g9v3-3b

LM Studio

Download the desired GGUF file and import it directly into LM Studio.

Credits

Original Model: ai9stars/G9v3-3B
GGUF Conversion & Quantization: NANI-Nithin
Quantization Framework: llama.cpp

Disclaimer

This repository contains converted GGUF files only.

Please refer to the original model repository for licensing terms, training methodology, benchmark results, intended use, limitations, and safety information.

Original model:

https://huggingface.co/ai9stars/G9v3-3B

Downloads last month: 947

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/G9v3-3B-GGUF

Base model

ai9stars/G9v3-3B

Quantized

(12)

this model