Instructions to use minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97/resolve/main/training_args.bin
- Command line
-
hf download hf://minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhtrannnn/1083730b-d469-41b9-abfc-ac331f02ab97/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- e8bca3e12b61c5d30c30cab1974cd75ee7a66fdf5fe5c5adef26ee0095f8aaaa
- Size of remote file:
- 6.78 kB
- SHA256:
- 0388c0aefca55e9f5678f599fdcfd18abb5aff6c6c55931d9c3518e326132cd8
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