Instructions to use SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3000/optimizer.pt from SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset: direct link, hf CLI and curl.
- Browser
- Download file 139 MB
-
https://huggingface.co/SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset/resolve/main/checkpoint-3000/optimizer.pt
- Command line
-
hf download hf://SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset/checkpoint-3000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SujanKarki/Qwen2.5-Coder-0.5B-Instruct_text_to_sql_qlora_newdataset/resolve/main/checkpoint-3000/optimizer.pt
139 MB
- Xet hash:
- 60c0fb19df105674a0e1305c7847bec145e9100b5fb5a873cfa01a83a3c1f1b7
- Size of remote file:
- 139 MB
- SHA256:
- d351099a3798654ff1917ca893289b525b327ab01b5030988b923bf8d89a86c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.