Image-Text-to-Text
Transformers
English
Chinese
eviscene
multimodal
image-factuality
agent
sft
distillation
Instructions to use ZiAnwang/EviScene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZiAnwang/EviScene with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ZiAnwang/EviScene")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZiAnwang/EviScene", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZiAnwang/EviScene with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZiAnwang/EviScene" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZiAnwang/EviScene", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZiAnwang/EviScene
- SGLang
How to use ZiAnwang/EviScene 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 "ZiAnwang/EviScene" \ --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": "ZiAnwang/EviScene", "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 "ZiAnwang/EviScene" \ --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": "ZiAnwang/EviScene", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZiAnwang/EviScene with Docker Model Runner:
docker model run hf.co/ZiAnwang/EviScene
Download .github/workflows/ci.yml from ZiAnwang/EviScene: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/ZiAnwang/EviScene/resolve/main/.github/workflows/ci.yml
- Command line
-
hf download hf://ZiAnwang/EviScene/.github/workflows/ci.yml
-
curl -L -o ci.yml https://huggingface.co/ZiAnwang/EviScene/resolve/main/.github/workflows/ci.yml
1.26 kB
| name: Code checks | |
| on: [push, pull_request] | |
| permissions: | |
| contents: read | |
| jobs: | |
| check: | |
| strategy: | |
| fail-fast: false | |
| matrix: | |
| os: [ubuntu-latest, windows-latest] | |
| python: ['3.11', '3.12'] | |
| runs-on: ${{ matrix.os }} | |
| steps: | |
| - uses: actions/checkout@v4 | |
| - uses: actions/setup-python@v5 | |
| with: | |
| python-version: ${{ matrix.python }} | |
| - run: python -m pip install -e ".[dev]" -e ./training | |
| - run: ruff check . | |
| - run: python scripts/check_release.py | |
| - run: python -m build | |
| - run: python -m build training | |
| - run: python scripts/check_release.py --distributions | |
| - name: Verify installed wheels outside checkout | |
| shell: bash | |
| run: | | |
| python -m venv wheel-check | |
| if [ "${{ runner.os }}" = "Windows" ]; then | |
| py="$PWD/wheel-check/Scripts/python.exe" | |
| else | |
| py="$PWD/wheel-check/bin/python" | |
| fi | |
| "$py" -m pip install dist/*.whl training/dist/*.whl | |
| cd "$RUNNER_TEMP" | |
| "$py" -c "import eviscene, eviscene_training; from eviscene.orchestrator.stage_runner import StageRunner, StageStep" | |
| "$py" -m eviscene --help | |
| "$py" -m eviscene_training.cli --help | |