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 eviscene/tools/base.py from ZiAnwang/EviScene: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/tools/base.py
- Command line
-
hf download hf://ZiAnwang/EviScene/eviscene/tools/base.py
-
curl -L -o base.py https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/tools/base.py
1.11 kB
| from __future__ import annotations | |
| from abc import ABC, abstractmethod | |
| from typing import Any, Dict | |
| from eviscene.orchestrator.source_access import SourceAccessPolicy | |
| class BaseTool(ABC): | |
| name: str = "" | |
| description: str = "" | |
| parameters: Dict[str, Any] = { | |
| "type": "object", | |
| "properties": {}, | |
| "required": [], | |
| } | |
| def call(self, params: Dict[str, Any]) -> Any: | |
| raise NotImplementedError | |
| def schema(self) -> Dict[str, Any]: | |
| return { | |
| "name": self.name, | |
| "description": self.description, | |
| "parameters": self.parameters, | |
| } | |
| def set_source_access_policy(self, policy: SourceAccessPolicy) -> None: | |
| """Bind a runtime retrieval policy without exposing it in the tool schema.""" | |
| self.source_access_policy = policy | |
| for attribute in ("client", "browse_client"): | |
| child = getattr(self, attribute, None) | |
| setter = getattr(child, "set_source_access_policy", None) | |
| if callable(setter): | |
| setter(policy) | |