Instructions to use qgyd2021/sft_llama2_stack_exchange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use qgyd2021/sft_llama2_stack_exchange with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("qgyd2021/sft_llama2_stack_exchange", set_active=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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I followed [this script](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/sft_llama2.py) to train this model.
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instead of the official [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) model, I used this repo [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf).
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The model trained on [lvwerra/stack-exchange-paired](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) dataset.
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seq_length: 1024
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I followed [this script](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/sft_llama2.py) to train this model.
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instead of the official [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) model, I used this repo [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf).
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The model trained on [lvwerra/stack-exchange-paired](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) dataset.
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seq_length: 1024
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