Instructions to use ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.2") model = PeftModel.from_pretrained(base_model, "ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5/resolve/main/adapter_model.bin
- Command line
-
hf download hf://ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/ajtaltarabukin2022/cd66053e-4ef0-447b-af7f-d9e2f1fd43d5/resolve/main/adapter_model.bin
168 MB
- Xet hash:
- e1dc9c1ccbffd2956de11be0c20b4ae215dc71a8c46d13cec1712c3cbbe1c95d
- Size of remote file:
- 168 MB
- SHA256:
- 6656dc8429316e09ce9fa5126927358f8ceb1190c77cc015ad97adf5fd038249
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