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/hf_publish.py from ldov/openjevv: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/ldov/openjevv/resolve/main/code/hf_publish.py
- Command line
-
hf download hf://ldov/openjevv/code/hf_publish.py
-
curl -L -o hf_publish.py https://huggingface.co/ldov/openjevv/resolve/main/code/hf_publish.py
1.56 kB
| #!/usr/bin/env python | |
| """Publish checkpoints / model card / results to the HF Hub repo. Token from HF_TOKEN env (never written to disk here). | |
| HF_TOKEN=... python hf_publish.py --repo AlexWortega/openjev --ckpt ckpt/qwen3.5-4b-nli --subdir qwen3.5-4b-nli | |
| HF_TOKEN=... python hf_publish.py --repo AlexWortega/openjev --files README.md results/*.json results/*.mp4 assets/*.png | |
| """ | |
| import argparse, glob, os | |
| from huggingface_hub import HfApi | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--repo", default="AlexWortega/openjev") | |
| ap.add_argument("--ckpt", default=None) | |
| ap.add_argument("--subdir", default=None, help="path in repo for the checkpoint (default: root)") | |
| ap.add_argument("--files", nargs="*", default=[]) | |
| ap.add_argument("--dest", default="", help="repo folder for --files") | |
| args = ap.parse_args() | |
| api = HfApi(token=os.environ["HF_TOKEN"]) | |
| api.create_repo(args.repo, repo_type="model", exist_ok=True) | |
| if args.ckpt: | |
| api.upload_folder(folder_path=args.ckpt, repo_id=args.repo, path_in_repo=args.subdir or "", repo_type="model", | |
| ignore_patterns=["*_trainer/*", "checkpoint-*"], commit_message=f"upload {os.path.basename(args.ckpt)}") | |
| print("uploaded", args.ckpt) | |
| for pat in args.files: | |
| for f in glob.glob(pat): | |
| api.upload_file(path_or_fileobj=f, path_in_repo=os.path.join(args.dest, os.path.basename(f)) if args.dest else os.path.basename(f) if not f.startswith("results/") else f, | |
| repo_id=args.repo, repo_type="model", commit_message=f"add {f}") | |
| print("uploaded", f) | |