Instructions to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428/resolve/main/tokenizer.model
- Command line
-
hf download hf://nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- 4747d621061ff864406560d79c8c3e355c05f2e88ba30eba88c8d5881d3d220d
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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