Instructions to use Malolmalsky/rag-hyp-commit-message-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Malolmalsky/rag-hyp-commit-message-generation with Transformers:

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

pipe = pipeline("text-generation", model="Malolmalsky/rag-hyp-commit-message-generation")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Malolmalsky/rag-hyp-commit-message-generation", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Malolmalsky/rag-hyp-commit-message-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Malolmalsky/rag-hyp-commit-message-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Malolmalsky/rag-hyp-commit-message-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Malolmalsky/rag-hyp-commit-message-generation

SGLang

How to use Malolmalsky/rag-hyp-commit-message-generation 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 "Malolmalsky/rag-hyp-commit-message-generation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Malolmalsky/rag-hyp-commit-message-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Malolmalsky/rag-hyp-commit-message-generation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Malolmalsky/rag-hyp-commit-message-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Malolmalsky/rag-hyp-commit-message-generation with Docker Model Runner:
```
docker model run hf.co/Malolmalsky/rag-hyp-commit-message-generation
```

Malolmalsky commited on 1 day ago

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@@ -4,13 +4,14 @@ base_model: facebook/rag-sequence-base
 datasets:
 - Malolmalsky/new-commits
 library_name: transformers
-pipeline_tag: text2text-generation
 tags:
 - rag
 - commit-message-generation
 - hyperbolic-geometry
 - software-maintenance
 - reproducible-research
 ---
 # RAG-Hyp Commit Message Generation Checkpoint

 datasets:
 - Malolmalsky/new-commits
 library_name: transformers
+pipeline_tag: text-generation
 tags:
 - rag
 - commit-message-generation
 - hyperbolic-geometry
 - software-maintenance
 - reproducible-research
+- text2text-generation
 ---
 # RAG-Hyp Commit Message Generation Checkpoint