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 adapter_model.bin from fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7: direct link, hf CLI and curl.
- Browser
- Download file 141 MB
-
https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/adapter_model.bin
- Command line
-
hf download hf://fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/fats-fme/a8285405-e96a-458b-8df5-55b8a6b1afe7/resolve/main/adapter_model.bin
141 MB
- Xet hash:
- 20ed1030a1232657d878e0783937385c007c2b295925760f03d3e5a86414c421
- Size of remote file:
- 141 MB
- SHA256:
- bca42c0456c3af310746a5aadd7cabebbc7e1ede890f244abb00862f6dcb106a
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