Instructions to use shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe: direct link, hf CLI and curl.
- Browser
- Download file 834 Bytes
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https://huggingface.co/shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe/resolve/0df9d2af9e910fb0ef4d5c4605c8ccc8468459f2/README.md
- Command line
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hf download hf://shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe@0df9d2af9e910fb0ef4d5c4605c8ccc8468459f2/README.md
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curl -L -o README.md https://huggingface.co/shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe/resolve/0df9d2af9e910fb0ef4d5c4605c8ccc8468459f2/README.md
834 Bytes
metadata
library_name: peft
tags:
- generated_from_trainer
base_model: Intel/neural-chat-7b-v3-3
model-index:
- name: shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe
results: []
shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2243
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