Instructions to use ejschwartz/slade-x86-O3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ejschwartz/slade-x86-O3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ejschwartz/slade-x86-O3") model = AutoModelForSeq2SeqLM.from_pretrained("ejschwartz/slade-x86-O3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ejschwartz/slade-x86-O3: direct link, hf CLI and curl.
- Browser
- Download file 747 MB
-
https://huggingface.co/ejschwartz/slade-x86-O3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ejschwartz/slade-x86-O3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ejschwartz/slade-x86-O3/resolve/main/pytorch_model.bin
747 MB
- Xet hash:
- 1c8a542aac684dfc22c68dbe5600936dd2f051ad420ebba6e7db692499b973c6
- Size of remote file:
- 747 MB
- SHA256:
- 23a91dca75e9ed2f0d99336557514a240f76124bb5b314392acf124195df89d2
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