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
- Xet hash:
- bcdfaf426387c543065afd2064da2c1bcee8224331c2c98f01dcba7c450c49df
- Size of remote file:
- 696 MB
- SHA256:
- 42a1d92de872ce85ff2bb1e189f8ac41fd3062e006827b15310484641e2b9157
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