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
```

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+---
+license: mit
+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
+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.