Instructions to use unflowerq/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use unflowerq/result_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1") model = PeftModel.from_pretrained(base_model, "unflowerq/result_model") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from unflowerq/result_model: direct link, hf CLI and curl.
- Browser
- Download file 5.56 kB
-
https://huggingface.co/unflowerq/result_model/resolve/main/training_args.bin
- Command line
-
hf download hf://unflowerq/result_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/unflowerq/result_model/resolve/main/training_args.bin
5.56 kB
- Xet hash:
- 0fdaaf3f6b27f1ca733d042cde78c91e06c734e2c25c1d31014493034276dc1d
- Size of remote file:
- 5.56 kB
- SHA256:
- fef8c3f9895ca6d4c9e1281b61ffeccd7baaba53398e623608cd6fa9c01e8c64
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