Mistral-7B-Summarization-QLoRa Open Source Model - Freely Assist in Scientific Literature Abstract Generation Tasks

Mistral 7B Summarization QLoRa

Developed by pkbiswas

A summarization generation model fine-tuned based on Mistral-7B-Instruct-v0.2, suitable for scientific literature summarization tasks

Text Generation

TensorBoard

Open Source License:Apache-2.0 #Scientific Literature Summarization #Instruction Fine-tuning #Low-resource Optimization

Downloads 39

Release Time : 11/3/2023

Model Overview

This model is a fine-tuned version of Mistral-7B-Instruct-v0.2 on the scitldr dataset, specifically designed for generating concise summaries of scientific literature

Model Features

Efficient Fine-tuning

Utilizes PEFT (Parameter-Efficient Fine-Tuning) technology for efficient fine-tuning

Scientific Literature Summarization

Specifically optimized for scientific literature summarization tasks

Low Resource Requirements

Lower resource demands compared to full fine-tuning

Model Capabilities

Text summarization generation

Scientific literature comprehension

English text processing

Use Cases

Academic Research

Paper Abstract Generation

Generates concise abstracts for scientific papers

Validated loss of 2.1059 on the scitldr dataset

Literature Review Assistance

Helps researchers quickly understand large volumes of literature content

Training Loss	Epoch	Step	Validation Loss
2.0732	0.1	200	2.1863
2.1324	0.2	400	2.1925
2.103	0.3	600	2.1876
2.0766	0.4	800	2.1737
2.0825	0.5	1000	2.1555
2.0731	0.6	1200	2.1465
2.0819	0.7	1400	2.1355
1.9802	0.8	1600	2.1223
2.0466	0.9	1800	2.1059

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Mistral 7B Summarization QLoRa

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Mistral Summarization

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📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

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