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.bin 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.bin
- Command line
-
hf download hf://cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/resolve/main/adapter_model.bin
671 MB
- Xet hash:
- 00d18d604cebbb3e111f8c5e1ef00e907603a60a48fd68ba6f73fc6afbb93e72
- Size of remote file:
- 671 MB
- SHA256:
- b63cf3e2d34c5f24fe2249341507349fcd468ef4a48d2acff489e1e85c4444a6
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