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 / README.md

Pavloffm

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	---
	language:
	- en
	license: mit
	library_name: transformers
	tags:
	- conventional-commits
	- qwen2.5-coder
	- text-generation
	- code-llm
	- fine-tuned
	---

	# 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

	```python
	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