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/optimizer.pt from fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7: direct link, hf CLI and curl.
- Browser
- Download file 71.9 MB
-
https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/last-checkpoint/optimizer.pt
71.9 MB
- Xet hash:
- 263ee65d67f3f41548192ce44ff2569a8d542d72c81761cbb137da4a537021ea
- Size of remote file:
- 71.9 MB
- SHA256:
- 1903dc654aa8b89bf98c0511d722b5fccdca6f504aa8cce06c73e592baeee830
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