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
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Download CONTRIBUTING.md from ZiAnwang/EviScene: direct link, hf CLI and curl.
- Browser
- Download file 688 Bytes
-
https://huggingface.co/ZiAnwang/EviScene/resolve/main/CONTRIBUTING.md
- Command line
-
hf download hf://ZiAnwang/EviScene/CONTRIBUTING.md
-
curl -L -o CONTRIBUTING.md https://huggingface.co/ZiAnwang/EviScene/resolve/main/CONTRIBUTING.md
688 Bytes
Contributing
Keep changes focused and validate behavior changes locally. Do not commit credentials, images, datasets, model weights, private annotations, or experiment logs. Do not change frozen prompts, schemas, or metric definitions as part of a formatting/refactoring change.
python -m pip install -e '.[dev]' -e ./training
ruff check .
python scripts/check_release.py
python -m build
python -m build training
CI checks code and packaging, not GPU training, external providers, or paper scores. Report those validations separately if performed. Test fixtures and internal validation suites are not included in this repository. Contributions are made under Apache-2.0.