Instructions to use Pavloffm/qwen-commit-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pavloffm/qwen-commit-merged with Transformers:

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

pipe = pipeline("text-generation", model="Pavloffm/qwen-commit-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pavloffm/qwen-commit-merged")
model = AutoModelForCausalLM.from_pretrained("Pavloffm/qwen-commit-merged")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Pavloffm/qwen-commit-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Pavloffm/qwen-commit-merged

SGLang

How to use Pavloffm/qwen-commit-merged 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 "Pavloffm/qwen-commit-merged" \
    --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": "Pavloffm/qwen-commit-merged",
		"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 "Pavloffm/qwen-commit-merged" \
        --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": "Pavloffm/qwen-commit-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pavloffm/qwen-commit-merged with Docker Model Runner:
```
docker model run hf.co/Pavloffm/qwen-commit-merged
```

Qwen Commit Merged - Conventional Commit Message Generator

Generates conventional commit messages from git diffs using a fine-tuned Qwen2.5-Coder-3B model. This is a standalone merged model (LoRA adapters merged into base model) ready for direct use.

Model Details

Model: Qwen2.5-Coder-3B fine-tuned on conventional commits
Fine-tuning Method: QLoRA (4-bit quantized, rank=8, alpha=16)
Training Data: 210 real conventional commits from open-source repositories
Model Size: ~3B parameters
Format: Merged model (no base model required)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained(
    "Pavloffm/qwen-commit-merged",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Pavloffm/qwen-commit-merged")

# Generate commit message
diff = """diff --git a/src/main.py b/src/main.py
index 1234567..abcdefg 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,3 +1,5 @@
+def new_feature():
+    pass
"""

messages = [{"role": "user", "content": f"Generate a conventional commit message for this diff:\n{diff}"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training

Base Model: Qwen/Qwen2.5-Coder-3B-Instruct
Epochs: 2
Learning rate: 1.5e-4
LoRA rank: 8, alpha: 16
Training examples: 210

License

MIT License

Downloads last month: 23

Safetensors

Model size

3B params

Tensor type

BF16