Instructions to use aalst/starcoder2-3b-peft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aalst/starcoder2-3b-peft-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-3b") model = PeftModel.from_pretrained(base_model, "aalst/starcoder2-3b-peft-lora") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aalst/starcoder2-3b-peft-lora: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/aalst/starcoder2-3b-peft-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://aalst/starcoder2-3b-peft-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aalst/starcoder2-3b-peft-lora/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- d16df1dd2b0a93b9c84274ab24207f6fec14a3fad21e7e10ff7c044814be6fc8
- Size of remote file:
- 5.05 kB
- SHA256:
- f4d8e5fb078487928a16d1178a0db1480e026e840d1270cbb9a4fdfa08845b4c
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