Instructions to use sharad/PP-ONNX-QNTZ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharad/PP-ONNX-QNTZ with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sharad/PP-ONNX-QNTZ") model = AutoModelForSeq2SeqLM.from_pretrained("sharad/PP-ONNX-QNTZ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model_quantized.onnx from sharad/PP-ONNX-QNTZ: direct link, hf CLI and curl.
- Browser
- Download file 237 MB
-
https://huggingface.co/sharad/PP-ONNX-QNTZ/resolve/main/decoder_model_quantized.onnx
- Command line
-
hf download hf://sharad/PP-ONNX-QNTZ/decoder_model_quantized.onnx
-
curl -L -o decoder_model_quantized.onnx https://huggingface.co/sharad/PP-ONNX-QNTZ/resolve/main/decoder_model_quantized.onnx
237 MB
- Xet hash:
- a2d9480c25673c609391e1f5bffb99d31e20881c56eb3c1282d11ee9eaf09786
- Size of remote file:
- 237 MB
- SHA256:
- d4eef834bedca76c938b3bf53657697841e9cf498988bdafb5241aa2a1244b16
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