Feature Extraction
Transformers
RWKV
English
hare
embeddings
text-retrieval
long-context
modernbert
streaming
semantic-search
retrieval
custom_code
Instructions to use SixOpen/HARE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SixOpen/HARE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SixOpen/HARE", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SixOpen/HARE", trust_remote_code=True, device_map="auto") - RWKV
How to use SixOpen/HARE with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 377 Bytes
f8ab83c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": true,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "TokenizersBackend",
"unk_token": "[UNK]"
}
|