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"

NANI-Nithin commited on Aug 12

Commit

99ca745

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1 Parent(s): 3fb3aca

Create README.md

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+---
+license: apache-2.0
+base_model:
+  - ai9stars/G9v3-3B
+library_name: llama.cpp
+tags:
+  - gguf
+  - llama-cpp
+  - text-generation
+  - conversational
+  - quantized
+  - 3b
+  - ai9stars
+language:
+  - en
+pipeline_tag: text-generation
+quantized_by: NANI-Nithin
+---
+# G9v3-3B-GGUF
+GGUF quantized releases of **[ai9stars/G9v3-3B](https://huggingface.co/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
+```bash
+./llama-cli \
+  -m G9v3-3B-Q4_K_M.gguf \
+  -p "Hello!"
+```
+### Ollama
+Create a Modelfile:
+```text
+FROM ./G9v3-3B-Q4_K_M.gguf
+```
+Then:
+```bash
+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