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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c10636a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Data interface / 数据接口
**Published dataset / 发布数据:** images + paired metadata; train/test are separate.
Labels and reference annotations must not enter the agent prompt.
图片配套元数据;训练集与测试集分开。标签和参考答案不进入 Agent 输入。
**Agent input / 推理输入:** `runtime_input/cases.jsonl`, one object per line:
```json
{"case_id":"sample-001","image_path":"images/sample-001.jpg","image_sha256":"<64-character SHA-256>"}
```
Only these three keys are allowed. Image paths are relative to `runtime_input/`.
The package also needs `manifest.json`; use
`python -m scripts.prepare_runtime_input --help` to build a local package from
metadata containing `case_id` and `image_path`. Other metadata is discarded.
仅保留这三个字段;转换工具不复制标签或私有标注,也不覆盖已有输出。
**Training / 训练:** SFT consumes reviewed trajectories, not image labels alone.
PSD consumes frozen student prefixes, completion tokens, teacher top-k targets,
and bound media; hints are teacher-only.
SFT 使用审核后的轨迹;PSD 使用冻结前缀、续写 token、教师 top-k 分布及绑定图片。
Judge annotations stay separate. See [training settings](../training/README.md).
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