Instructions to use bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16/resolve/57a4a23b934ea6c3f25615e13a6979d55c48fd68/training_args.bin
- Command line
-
hf download hf://bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16@57a4a23b934ea6c3f25615e13a6979d55c48fd68/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bharati2324/Qwen2.5-1.5B-Instruct-Code-LoRA-r16/resolve/57a4a23b934ea6c3f25615e13a6979d55c48fd68/training_args.bin
5.5 kB
- Xet hash:
- 89449cd5566e4193f31078789542dba1632ea09c7708c0477a5007d5b34c1889
- Size of remote file:
- 5.5 kB
- SHA256:
- b94428b1f11415134e484f11c64f184301f12535cb366e2bb80570d6fc2ccec6
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