Feature Extraction
Transformers
Safetensors
llama_bidirec
mergekit
Merge
custom_code
text-embeddings-inference
8-bit precision
Instructions to use KwangHwi/quantization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KwangHwi/quantization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KwangHwi/quantization", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KwangHwi/quantization", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from KwangHwi/quantization: direct link, hf CLI and curl.
- Browser
- Download file 313 Bytes
-
https://huggingface.co/KwangHwi/quantization/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://KwangHwi/quantization/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/KwangHwi/quantization/resolve/main/1_Pooling/config.json
313 Bytes
| { | |
| "word_embedding_dimension": 2048, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |