Instructions to use aac6fef/laya-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aac6fef/laya-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir laya-mlx aac6fef/laya-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer/tokenizer_config.json from aac6fef/laya-mlx: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/aac6fef/laya-mlx/resolve/main/tokenizer/tokenizer_config.json
- Command line
-
hf download hf://aac6fef/laya-mlx/tokenizer/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/aac6fef/laya-mlx/resolve/main/tokenizer/tokenizer_config.json
308 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } |