Instructions to use CyrusCheungkf/git-commit-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CyrusCheungkf/git-commit-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CyrusCheungkf/git-commit-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CyrusCheungkf/git-commit-3B")
model = AutoModelForCausalLM.from_pretrained("CyrusCheungkf/git-commit-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use CyrusCheungkf/git-commit-3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CyrusCheungkf/git-commit-3B",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use CyrusCheungkf/git-commit-3B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CyrusCheungkf/git-commit-3B
# Run inference directly in the terminal:
llama-cli -hf CyrusCheungkf/git-commit-3B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CyrusCheungkf/git-commit-3B
# Run inference directly in the terminal:
llama-cli -hf CyrusCheungkf/git-commit-3B

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 CyrusCheungkf/git-commit-3B
# Run inference directly in the terminal:
./llama-cli -hf CyrusCheungkf/git-commit-3B

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 CyrusCheungkf/git-commit-3B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CyrusCheungkf/git-commit-3B

Use Docker

docker model run hf.co/CyrusCheungkf/git-commit-3B

LM Studio
Jan

vLLM

How to use CyrusCheungkf/git-commit-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CyrusCheungkf/git-commit-3B

SGLang

How to use CyrusCheungkf/git-commit-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CyrusCheungkf/git-commit-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CyrusCheungkf/git-commit-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CyrusCheungkf/git-commit-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CyrusCheungkf/git-commit-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CyrusCheungkf/git-commit-3B with Ollama:
```
ollama run hf.co/CyrusCheungkf/git-commit-3B
```

Unsloth Studio new

How to use CyrusCheungkf/git-commit-3B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CyrusCheungkf/git-commit-3B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CyrusCheungkf/git-commit-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CyrusCheungkf/git-commit-3B to start chatting

Pi new

How to use CyrusCheungkf/git-commit-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CyrusCheungkf/git-commit-3B

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CyrusCheungkf/git-commit-3B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CyrusCheungkf/git-commit-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CyrusCheungkf/git-commit-3B

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 CyrusCheungkf/git-commit-3B

Run Hermes

hermes

Docker Model Runner
How to use CyrusCheungkf/git-commit-3B with Docker Model Runner:
```
docker model run hf.co/CyrusCheungkf/git-commit-3B
```

Lemonade

How to use CyrusCheungkf/git-commit-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CyrusCheungkf/git-commit-3B

Run and chat with the model

lemonade run user.git-commit-3B-{{QUANT_TAG}}

List all available models

lemonade list

CyrusCheungkf commited on Apr 17, 2025

Commit

4db9777

verified ·

1 Parent(s): 72a6492

Update README.md

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@@ -1,199 +1,56 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 library_name: transformers
+tags:
+- git
+license: mit
+datasets:
+- Maxscha/commitbench
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-3B-Instruct
+pipeline_tag: text-generation
 ---
 # Model Card for Model ID
+Fine tuned Qwen2.5 3B model for writing git commit message. Used dataset Maxscha/commitbench
 ## Model Details
+- **Developed by:** Cyrus Cheung
+- **Model type:** Qwen2.5 3B
+- **License:** MIT
+- **Finetuned from model:** Qwen/Qwen2.5-Coder-3B-Instruct
 ## Uses
+```python
+from transformers.models.auto.modeling_auto import AutoModelForCausalLM
+from transformers.models.auto.tokenization_auto import AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("CyrusCheungkf/git-commit-3B")
+tokenizer = AutoTokenizer.from_pretrained("CyrusCheungkf/git-commit-3B")
+git_diff = "Output from using 'git diff'"
+INSTRUCTION = """You are Git Commit Message Pro, a specialist in crafting precise, professional Git commit messages from .diff files. Your role is to analyze these files, interpret the changes, and generate a clear, direct commit message.
+Guidelines:
+1. Be specific about the type of change (e.g., "Rename variable X to Y", "Extract method Z from class W").
+2. Prefer to write it on why and how instead of what changed.
+3. Interpret the changes; do not transcribe the diff.
+4. If you cannot read the entire file, attempt to generate a message based on the available information.
+5. Be concise and summarize the most important changes. Keep your response in 1 sentence."""
+conversation = [
+  {"role": "user", "content": INSTRUCTION + "\n\nInputs:\n" + git_diff},
+]
+tokens = tokenizer.apply_chat_template(
+  conversation, add_generation_prompt=True, return_tensors="pt", return_dict=True
+)
+output = model.generate(
+  inputs=tokens["input_ids"],
+  attention_mask=tokens["attention_mask"],
+)
+print(output)
+```