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.