Mistral-7B-Instruct-v0.2-4b-r16-task1640

Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1640

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Model Details

Model Description

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LoRA trained on task1640_aqa1.0_answerable_unanswerable_question_classification

• 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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

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/task1640_aqa1.0_answerable_unanswerable_question_classification 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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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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