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 training_args.bin from cimol/360894cd-0ff4-44eb-a921-80e0e73d052e: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/360894cd-0ff4-44eb-a921-80e0e73d052e/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 4834dc9c18c0a3e6aa3552abeab9c55b71e29252aeaa3d19a9262211356d8369
- Size of remote file:
- 6.84 kB
- SHA256:
- 4fdfe94d0261830fb8ee21c67735a089af1741341043dd92b589ec5f6c03b7a1
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