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
| { | |
| "base_model": "Alibaba-NLP/gte-modernbert-base", | |
| "variant": "conservative", | |
| "hidden_size": 768, | |
| "num_heads": 12, | |
| "replaced_layers": { | |
| "1": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "2": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "4": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "5": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "7": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "8": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "10": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "11": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "13": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "14": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "16": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "17": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "19": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| }, | |
| "20": { | |
| "was_global": false, | |
| "transferred": [ | |
| "Q->R", | |
| "K->K", | |
| "V->V", | |
| "O->O" | |
| ] | |
| } | |
| }, | |
| "total_params": 173872910 | |
| } |