Instructions to use shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/NousResearch/Yarn-Mistral-7b-128k") model = PeftModel.from_pretrained(base_model, "shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef: direct link, hf CLI and curl.
- Browser
- Download file 842 Bytes
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https://huggingface.co/shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef/resolve/main/README.md
- Command line
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hf download hf://shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef/README.md
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curl -L -o README.md https://huggingface.co/shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef/resolve/main/README.md
842 Bytes
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: NousResearch/Yarn-Mistral-7b-128k | |
| model-index: | |
| - name: shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3707 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.0+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |