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
File size: 1,670 Bytes
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 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # -*- coding: utf-8 -*-
"""CLI for rendering one or more trace JSON files into HTML viewers."""
from __future__ import annotations
import argparse
import os
from typing import Iterable, List
from eviscene.trace_viewer import render_trace_file
def _iter_trace_files(path: str) -> Iterable[str]:
if os.path.isfile(path):
if path.lower().endswith(".json"):
yield path
return
for root, _, files in os.walk(path):
for name in files:
if name.lower().endswith(".json"):
yield os.path.join(root, name)
def main() -> None:
parser = argparse.ArgumentParser(description="Render trace JSON files to standalone HTML viewers")
parser.add_argument("input_path", help="A trace JSON file or a directory containing trace JSON files")
parser.add_argument("--output-dir", default="", help="Optional output directory for generated HTML files")
args = parser.parse_args()
trace_files: List[str] = list(_iter_trace_files(args.input_path))
if not trace_files:
raise SystemExit(f"No trace JSON files found under: {args.input_path}")
generated = 0
for json_path in trace_files:
if args.output_dir:
os.makedirs(args.output_dir, exist_ok=True)
html_name = os.path.splitext(os.path.basename(json_path))[0] + ".html"
html_path = os.path.join(args.output_dir, html_name)
else:
html_path = os.path.splitext(json_path)[0] + ".html"
render_trace_file(json_path, html_path)
print(html_path)
generated += 1
print(f"Rendered {generated} HTML file(s).")
if __name__ == "__main__":
main()
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