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 config_sentence_transformers.json from KwangHwi/quantization: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/KwangHwi/quantization/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://KwangHwi/quantization/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/KwangHwi/quantization/resolve/main/config_sentence_transformers.json
299 Bytes
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.0.1", | |
| "transformers": "4.47.1", | |
| "pytorch": "2.9.1+cu126" | |
| }, | |
| "prompts": { | |
| "query": "query: ", | |
| "document": "passage: " | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |