Instructions to use winglian/scout-qlora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use winglian/scout-qlora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16") model = PeftModel.from_pretrained(base_model, "winglian/scout-qlora-adapter") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from winglian/scout-qlora-adapter: direct link, hf CLI and curl.
- Browser
- Download file 27.9 MB
-
https://huggingface.co/winglian/scout-qlora-adapter/resolve/main/tokenizer.json
- Command line
-
hf download hf://winglian/scout-qlora-adapter/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/winglian/scout-qlora-adapter/resolve/main/tokenizer.json
27.9 MB
- Xet hash:
- 8898b7063e3c1eba0efe7e0b2dba3bb8f6649c2aff7ceaea556b717f58ce91ed
- Size of remote file:
- 27.9 MB
- SHA256:
- 172c9eb4beafc72601690da3ccfcede5c2e6806a8d5ec1fca33e22acea8023a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.