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/integrations/clock/system_clock.py from ZiAnwang/EviScene: direct link, hf CLI and curl.
- Browser
- Download file 931 Bytes
-
https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/integrations/clock/system_clock.py
- Command line
-
hf download hf://ZiAnwang/EviScene/eviscene/integrations/clock/system_clock.py
-
curl -L -o system_clock.py https://huggingface.co/ZiAnwang/EviScene/resolve/main/eviscene/integrations/clock/system_clock.py
931 Bytes
| from __future__ import annotations | |
| from eviscene.environment import get_env | |
| from dataclasses import dataclass | |
| from datetime import date, datetime | |
| from typing import Dict | |
| class SystemClockClient: | |
| def now(self) -> Dict[str, str]: | |
| current = datetime.now().astimezone() | |
| override = get_env("EVISCENE_RUNTIME_DATE", "").strip() | |
| if override: | |
| runtime_date = date.fromisoformat(override) | |
| current = current.replace( | |
| year=runtime_date.year, | |
| month=runtime_date.month, | |
| day=runtime_date.day, | |
| ) | |
| return { | |
| "current_date": current.date().isoformat(), | |
| "current_datetime": current.isoformat(), | |
| "timezone": str(current.tzinfo), | |
| "source": ( | |
| "runtime_date_override" | |
| if override | |
| else "system_clock" | |
| ), | |
| } | |