Text Classification
Transformers
Safetensors
English
tiny_jev
feature-extraction
jev
system-one
open-jev
decision-model
calibrated
routing
classification
guardrails
non-autoregressive
custom_code
Instructions to use lostargon/Tiny-Jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lostargon/Tiny-Jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lostargon/Tiny-Jev", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lostargon/Tiny-Jev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from lostargon/Tiny-Jev: direct link, hf CLI and curl.
- Browser
- Download file 423 Bytes
-
https://huggingface.co/lostargon/Tiny-Jev/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://lostargon/Tiny-Jev/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/lostargon/Tiny-Jev/resolve/main/tokenizer_config.json
423 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<opt>" | |
| ], | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |