Instructions to use chargoddard/ypotryll-22b-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chargoddard/ypotryll-22b-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("chargoddard/llama2-22b-blocktriangular") model = PeftModel.from_pretrained(base_model, "chargoddard/ypotryll-22b-qlora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b6644087e440ff0c8cd5a546da8584164b51d90e9e01f7028bda5dc116cce0a
- Size of remote file:
- 2.6 GB
- SHA256:
- 8fa6d5f99b97baefa59dff09371cf6dc1f455e6534fd2f74fa741b368b05980d
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