Instructions to use cimol/360894cd-0ff4-44eb-a921-80e0e73d052e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/360894cd-0ff4-44eb-a921-80e0e73d052e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tokyotech-llm/Llama-3-Swallow-8B-v0.1") model = PeftModel.from_pretrained(base_model, "cimol/360894cd-0ff4-44eb-a921-80e0e73d052e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from cimol/360894cd-0ff4-44eb-a921-80e0e73d052e: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/resolve/main/adapter_model.safetensors
671 MB
- Xet hash:
- 64b6b91c5f0f4a74778eaa2c925d149286b8b484c6d2e3146251294fc78dfa13
- Size of remote file:
- 671 MB
- SHA256:
- 6ab5a6bbb5c3de18c0b93531cd9a042ca59160d402ad50b37dee2f1d15cefee6
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