Instructions to use eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-7b-it") model = PeftModel.from_pretrained(base_model, "eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/trainer_state.json from eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07: direct link, hf CLI and curl.
- Browser
- Download file 5.72 kB
-
https://huggingface.co/eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07/resolve/1f28a4754bf671ed6da53bb71369a581c3e1cf4d/last-checkpoint/trainer_state.json
- Command line
-
hf download hf://eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07@1f28a4754bf671ed6da53bb71369a581c3e1cf4d/last-checkpoint/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/eeeebbb2/f1870551-e865-4496-82eb-3122c4c5fe07/resolve/1f28a4754bf671ed6da53bb71369a581c3e1cf4d/last-checkpoint/trainer_state.json
5.72 kB
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