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 tokenizer.json from KwangHwi/quantization: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/KwangHwi/quantization/resolve/main/tokenizer.json
- Command line
-
hf download hf://KwangHwi/quantization/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/KwangHwi/quantization/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 662287c8a7db7afa36118cd4e9bc68c53662e7211a807aeb6a0dc86ebb199ff4
- Size of remote file:
- 17.2 MB
- SHA256:
- cb956fd3e742594d4410fdb86df5edf0c1b0f1f382117a2fbd877f2372ec158d
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