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task083_babi_t1_single_supporting_fact_answer_generation
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language: en
license: mit
library_name: pytorch

Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task083

Model Details

Model Description

LoRA trained on task083_babi_t1_single_supporting_fact_answer_generation

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

Model Sources [optional]

  • 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

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

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

Use the code below to get started with the model.

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Training Details

Training Data

"task083_babi_t1_single_supporting_fact_answer_generation" sourced from https://github.com/allenai/natural-instructions

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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

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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