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)# 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 __pycache__/llama_bidirectional_model.cpython-312.pyc from KwangHwi/quantization: direct link, hf CLI and curl.
- Browser
- Download file 9.14 kB
-
https://huggingface.co/KwangHwi/quantization/resolve/main/__pycache__/llama_bidirectional_model.cpython-312.pyc
- Command line
-
hf download hf://KwangHwi/quantization/__pycache__/llama_bidirectional_model.cpython-312.pyc
-
curl -L -o llama_bidirectional_model.cpython-312.pyc https://huggingface.co/KwangHwi/quantization/resolve/main/__pycache__/llama_bidirectional_model.cpython-312.pyc
9.14 kB
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