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This model aims to optimize QA & summerization tasks for the capstone project "Edge LLM - Reducing LLM Memory Footprint to < 2GB" in UW, sponsered by Amazon.

Model Details

Model Description

Base model is Fighoture/Llama-2-7b-chat-shortgpt-with-angular-25-percent, which has been pruned with shortgpt by 25%(8) layers according to angular distance.

This model is fine-tuned by a dataset combination including:

randomly-selected 5k sample of sharegpt dataset. Link is as followed: https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
randomly-selected 2.5k sample of Open-Orca dataset, filtered by allenai/tulu-v2-sft-mixture
randomly-selected 2.5k sample of GPT4-Alpaca dataset, filtered by allenai/tulu-v2-sft-mixture

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How to Get Started with the Model

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Training Details

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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