Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 335 Bytes
-
https://huggingface.co/Q1z/Pivot/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Q1z/Pivot/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Q1z/Pivot/resolve/main/tokenizer_config.json
335 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<|mask|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|pad|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |