Mistral-7B-Instruct-v0.2-4b-r16-task398

Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task398

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Model Details

Model Description

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LoRA trained on task398_semeval_2018_task1_tweet_joy_detection

• Developed by: bruel
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• Model type: LoRA
• Language(s) (NLP): en
• License: mit
• Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2

Model Sources [optional]

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• Repository: https://github.com/bruel-gabrielsson
• Paper [optional]: "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon
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Uses

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Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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https://huggingface.co/datasets/Lots-of-LoRAs/task398_semeval_2018_task1_tweet_joy_detection sourced from https://github.com/allenai/natural-instructions

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

@misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, }

APA:

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Glossary [optional]

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