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LAION-AI
Model Guide - Inference, Finetuning and Training
Task:
Create a Guide for Model Identification and Running Different Sized Models
Problem Statement:
It can be difficult for OA'ers to keep track of the different models and their associated sizes, as well as how to run them efficiently.
Proposed Solution:
To address this issue, we should create a comprehensive guide for model identification and running different sized models. This guide should include:
- An overview of the most commonly used language models, including their size and intended use cases.
- A comparison of the computational resources required to run different sized models, including GPU VRAM requirements.
- A step-by-step guide on how to identify the right model for a specific task.
- Tips and best practices for running different sized models, including considerations for batch size and number of layers.
Deliverables:
A markdown file that outlines the guide for model identification and running different sized models. Example code for running language models using popular NLP libraries such as PyTorch and TensorFlow.
Timeline:
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Benefits:
- Provides a useful resource for OA'ers who are new to the field of LLM's or who are working with language models for the first time.
- Helps OA'ers make informed decisions about which language model to use for a specific task.
- Improves efficiency and productivity by providing best practices for running different sized models.
