Instructions to use mzzavaa/otto_model_demo_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mzzavaa/otto_model_demo_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "mzzavaa/otto_model_demo_2") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mzzavaa/otto_model_demo_2: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/mzzavaa/otto_model_demo_2/resolve/main/tokenizer.json
- Command line
-
hf download hf://mzzavaa/otto_model_demo_2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mzzavaa/otto_model_demo_2/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 50f4e1c5a0180e175c4cfd2605996dbf5a6c1ae8a80d0cd6943d16b0b2b5d9e9
- Size of remote file:
- 17.2 MB
- SHA256:
- 4bda08a40f3f483c50d4c9f5e9df8c88ed16fe1ddf5e00cd5436910d071137f3
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