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
```

rag-hyp-commit-message-generation

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---
license: mit
base_model: facebook/rag-sequence-base
datasets:
- Malolmalsky/new-commits
library_name: transformers
pipeline_tag: text-generation
tags:
- rag
- commit-message-generation
- hyperbolic-geometry
- software-maintenance
- reproducible-research
---

# RAG-Hyp Commit Message Generation Checkpoint

This repository stores the heavyweight checkpoint for the RAG-Hyp dissertation
artifact. The source code, reproduction scripts, experiment matrix, and
method-to-code traceability documentation are kept in the companion code
repository.

## Files

| File | Size, bytes | SHA-256 |
|---|---:|---|
| `checkpoint-170000/model.safetensors` | `2061032996` | `4f1b9e1837998652bdbf6fdf1aa9fc3e006b99d72d312fcb11eab7048e73b1ef` |
| `checkpoint-170000/config.json` | `5959` | `d4d3f41b44c41c7795a2717e6f5c8d0bebf93f5cf0f3f0e6c0ebad720aaaf93b` |

## Data

The public commit dataset used by the reproduction pipeline is:

- `Malolmalsky/new-commits`
- <https://huggingface.co/datasets/Malolmalsky/new-commits>

## Base Model

The checkpoint is based on `facebook/rag-sequence-base` and is intended to be loaded by the
RAG-Hyp runtime from the companion reproducibility repository.

## Loading

```bash
python3 - <<'PY'
from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="Malolmalsky/rag-hyp-commit-message-generation",
    allow_patterns=["checkpoint-170000/*", "artifact_manifest.json"],
)
print(path)
PY
```

Then point the runtime to the downloaded checkpoint:

```bash
export RAG_HYP_MODEL_PATH=/path/to/snapshot/checkpoint-170000
```

## Reproducibility

`artifact_manifest.json` records file sizes, SHA-256 hashes, the source dataset,
and the base model identifier.