Instructions to use nihar-damera/commit-suggestor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nihar-damera/commit-suggestor with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("nihar-damera/commit-suggestor")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nihar-damera/commit-suggestor 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 nihar-damera/commit-suggestor:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nihar-damera/commit-suggestor:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nihar-damera/commit-suggestor:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nihar-damera/commit-suggestor:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf nihar-damera/commit-suggestor:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf nihar-damera/commit-suggestor:Q4_K_M

Use Docker

docker model run hf.co/nihar-damera/commit-suggestor:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use nihar-damera/commit-suggestor with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nihar-damera/commit-suggestor:Q4_K_M

Ollama
How to use nihar-damera/commit-suggestor with Ollama:
```
ollama run hf.co/nihar-damera/commit-suggestor:Q4_K_M
```
Unsloth Desktop

How to use nihar-damera/commit-suggestor with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nihar-damera/commit-suggestor"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nihar-damera/commit-suggestor"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nihar-damera/commit-suggestor with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nihar-damera/commit-suggestor"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nihar-damera/commit-suggestor"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nihar-damera/commit-suggestor",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use nihar-damera/commit-suggestor with Docker Model Runner:
```
docker model run hf.co/nihar-damera/commit-suggestor:Q4_K_M
```

Lemonade

How to use nihar-damera/commit-suggestor with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nihar-damera/commit-suggestor:Q4_K_M

Run and chat with the model

lemonade run user.commit-suggestor-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nihar-damera/commit-suggestor with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nihar-damera/commit-suggestor"

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 nihar-damera/commit-suggestor

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nihar-damera/commit-suggestor with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nihar-damera/commit-suggestor"

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 "nihar-damera/commit-suggestor" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

commit-suggestor

File size: 129 Bytes

{
  "name": "dpo-beta-0.3",
  "prompt_version": 2,
  "repo_context": false,
  "quantization": "Q4_K_M",
  "llama_cpp": "b11701"
}