Instructions to use andstor/bigcode-starcoderbase-unit-test-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use andstor/bigcode-starcoderbase-unit-test-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase") model = PeftModel.from_pretrained(base_model, "andstor/bigcode-starcoderbase-unit-test-lora") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from andstor/bigcode-starcoderbase-unit-test-lora: direct link, hf CLI and curl.
- Browser
- Download file 32.1 MB
-
https://huggingface.co/andstor/bigcode-starcoderbase-unit-test-lora/resolve/main/adapter_model.bin
- Command line
-
hf download hf://andstor/bigcode-starcoderbase-unit-test-lora/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/andstor/bigcode-starcoderbase-unit-test-lora/resolve/main/adapter_model.bin
32.1 MB
- Xet hash:
- 8d03f4a190f04141ea505827f073a5978600ca54882711ddf7af87b7b20096ca
- Size of remote file:
- 32.1 MB
- SHA256:
- 0e769593e99c1ffcaa70b228bfd970eb1edd53fd867d8a97acaffcf1ced6bee5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.