Instructions to use fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-llama2-13b-v8.1-fp16") model = PeftModel.from_pretrained(base_model, "fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b: direct link, hf CLI and curl.
- Browser
- Download file 568 kB
-
https://huggingface.co/fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/resolve/main/tokenizer.model
- Command line
-
hf download hf://fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/resolve/main/tokenizer.model
568 kB
- Xet hash:
- 4df1b8cd95f7e9208fc83d05c1dbc06d6afc347b59abdd8c2ac75b9185e48ff5
- Size of remote file:
- 568 kB
- SHA256:
- f440c53d2cc6f14a7ed7124dea5f5a7402fb4fc95bccb5d8be6d0f7e74d327ed
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