Instructions to use miladalsh/llam3-conv-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use miladalsh/llam3-conv-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "miladalsh/llam3-conv-model") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from miladalsh/llam3-conv-model: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/miladalsh/llam3-conv-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://miladalsh/llam3-conv-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/miladalsh/llam3-conv-model/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- b7ef22de27d7f300f1b700846c49688e96e7a496f25fe0a67badf02257b2581a
- Size of remote file:
- 17.2 MB
- SHA256:
- 9d20d6269ae0cc1956acf3b97d290486670787052089ce415eadeac76afb0dea
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