Feature Extraction
Transformers
Safetensors
llama_bidirec
mergekit
Merge
custom_code
text-embeddings-inference
8-bit precision
quanto
Instructions to use KwangHwi/quantization_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KwangHwi/quantization_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KwangHwi/quantization_v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KwangHwi/quantization_v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f22aa2fa505da4a98de1f550fc995d6b0a6b844ababd390d43f8acc97b5ae89b
- Size of remote file:
- 1.5 GB
- SHA256:
- 79b68957119a1ba2974b5b2846956975657c6c375c9ba47ee70ee76c59a091cf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.