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/current_time.py from ZiAnwang/EviScene: direct link, hf CLI and curl.
- Browser
- Download file 996 Bytes
-
https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/tools/current_time.py
- Command line
-
hf download hf://ZiAnwang/EviScene/eviscene/tools/current_time.py
-
curl -L -o current_time.py https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/tools/current_time.py
996 Bytes
| from __future__ import annotations | |
| from dataclasses import dataclass, field | |
| from typing import Dict | |
| from eviscene.integrations.clock.system_clock import SystemClockClient | |
| from eviscene.tools.base import BaseTool | |
| class CurrentTimeTool(BaseTool): | |
| """Return runtime current time from the system clock.""" | |
| client: SystemClockClient | None = None | |
| name: str = "current_time" | |
| description: str = "Return the current runtime date, datetime, and timezone from the system clock." | |
| parameters: dict = field( | |
| default_factory=lambda: { | |
| "type": "object", | |
| "properties": {}, | |
| "required": [], | |
| } | |
| ) | |
| def __post_init__(self) -> None: | |
| if self.client is None: | |
| self.client = SystemClockClient() | |
| def get_current_time(self) -> Dict[str, str]: | |
| return {"status": "success", **self.client.now()} | |
| def call(self, params: Dict[str, str]) -> Dict[str, str]: | |
| return self.get_current_time() | |