Instructions to use EdBergJr/Llama32B_Baha_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use EdBergJr/Llama32B_Baha_3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "EdBergJr/Llama32B_Baha_3") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from EdBergJr/Llama32B_Baha_3: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/EdBergJr/Llama32B_Baha_3/resolve/main/tokenizer.json
- Command line
-
hf download hf://EdBergJr/Llama32B_Baha_3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/EdBergJr/Llama32B_Baha_3/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 01ebab01bee1b6a8a36f217f20dfe198b6d2dbf9ebc578c5d1ef65b774061cc2
- Size of remote file:
- 17.2 MB
- SHA256:
- 13574c77ca1d572525cfa7caac46cee99309100524dad568a7ef85ae383df39f
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