Instructions to use jbochi/candle-coedit-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbochi/candle-coedit-quantized with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jbochi/candle-coedit-quantized") model = AutoModelForSeq2SeqLM.from_pretrained("jbochi/candle-coedit-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from jbochi/candle-coedit-quantized: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/jbochi/candle-coedit-quantized/resolve/main/model.safetensors
- Command line
-
hf download hf://jbochi/candle-coedit-quantized/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/jbochi/candle-coedit-quantized/resolve/main/model.safetensors
3.13 GB
- Xet hash:
- e9eba527b52af1ea7294bf4b63bfda2c004d4031b7a8d03a9b2b1958c9b78ccb
- Size of remote file:
- 3.13 GB
- SHA256:
- c692f68bca5c6899801bc9fad626fefd3359ccd95e26dccae4dd72186fd98852
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