Instructions to use fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/trainer_state.json from fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7: direct link, hf CLI and curl.
- Browser
- Download file 4.89 kB
-
https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/last-checkpoint/trainer_state.json
- Command line
-
hf download hf://fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/last-checkpoint/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/last-checkpoint/trainer_state.json
4.89 kB
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