Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
Instructions to use ldov/openjevv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldov/openjevv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldov/openjevv")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ldov/openjevv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/sglang_openjev/__init__.py from ldov/openjevv: direct link, hf CLI and curl.
- Browser
- Download file 176 Bytes
-
https://huggingface.co/ldov/openjevv/resolve/main/code/sglang_openjev/__init__.py
- Command line
-
hf download hf://ldov/openjevv/code/sglang_openjev/__init__.py
-
curl -L -o __init__.py https://huggingface.co/ldov/openjevv/resolve/main/code/sglang_openjev/__init__.py
176 Bytes
| """SGLang external model package for openjev. Enable with SGLANG_EXTERNAL_MODEL_PACKAGE=sglang_openjev | |
| (this directory's parent must be on PYTHONPATH); see serve_sglang.sh.""" | |