Instructions to use bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3") model = PeftModel.from_pretrained(base_model, "bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e/resolve/main/training_args.bin
- Command line
-
hf download hf://bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bane5631/e525d81c-2731-44fa-9a33-4b1a312ea95e/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- d83e37a85070c50f79f39901bccd2281efc25e6b7c447a208b609e5ac9394455
- Size of remote file:
- 6.78 kB
- SHA256:
- 25099da43392d2edf97806f55f75e7d639bf1873caa538246aaa5141ad80f4cb
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