LLMsPracticalGuide

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A curated list of practical guide resources of LLMs

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Type

Open Source

Visit Website GitHub (38 stars)

About LLMsPracticalGuide

LLMsPracticalGuide is a curated and actively updated GitHub repository that provides a comprehensive collection of practical guide resources for Large Language Models (LLMs). It is based on a survey paper titled 'Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond' and includes an evolutionary tree of modern LLMs. The repository covers practical guides for models (BERT-style and GPT-style), data (pretraining, finetuning, test), NLP tasks (traditional NLU, generation, knowledge-intensive, real-world tasks), efficiency, trustworthiness, benchmarks, instruction tuning, safety alignment (harmlessness, truthfulness, helpfulness), and usage restrictions (commercial and research licensing). It aims to help practitioners navigate the vast landscape of LLMs and their applications in natural language processing.

Key Features

Curated list of practical LLM guides based on a comprehensive survey paper

Evolutionary tree of modern LLMs tracing model development

Covers BERT-style and GPT-style model architectures

Guidance on pretraining, finetuning, and test data

Practical advice for traditional NLU, generation, and knowledge-intensive tasks

Sections on efficiency, trustworthiness, benchmarks, and instruction tuning

Safety alignment coverage: harmlessness, truthfulness, helpfulness

Usage and restrictions section with licensing information for models and data

Actively updated with latest news and contributions

Pros & Cons

Pros

Comprehensive coverage of practical LLM topics from models to deployment
Based on a peer-reviewed survey paper, ensuring accuracy and depth
Actively maintained and updated with new developments
Includes usage restrictions and licensing details for responsible use
Open source and free to access on GitHub
Visual evolutionary tree aids understanding of LLM lineage

Cons

Not a tool or API but a curated guide; requires self-implementation
May be overwhelming for beginners due to breadth and technical depth
Relies on external papers and resources; some links may become outdated
Limited to textual information; no interactive demos or code execution

Best For

Navigating the landscape of large language models for NLP practitionersSelecting appropriate LLM architectures (BERT-style vs GPT-style) for specific tasksPreparing and handling pretraining, finetuning, and test dataUnderstanding and applying LLMs to traditional NLU, generation, and knowledge-intensive tasksImplementing alignment techniques for safety and trustworthinessBenchmarking and evaluating LLM performanceResearching the evolution and capabilities of LLMs

FAQ

What is the LLMsPracticalGuide repository?

It is a curated list of practical guide resources for Large Language Models (LLMs), based on the survey paper 'Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond'.

What topics does the guide cover?

It covers practical guides for models (BERT-style and GPT-style), data (pretraining, finetuning, test), NLP tasks (traditional NLU, generation, knowledge-intensive), efficiency, trustworthiness, benchmarks, instruction tuning, safety alignment (harmlessness, truthfulness, helpfulness), and usage restrictions including licensing.

Is this repository free to use?

Yes, it is an open-source GitHub repository available for free.

How is the repository structured?

The README provides a table of contents with links to sections: Practical Guide for Models, Practical Guide for Data, Practical Guide for NLP Tasks, Efficiency, Trustworthiness, Benchmark, Instruction Tuning, Alignment, Prompting, and Usage and Restrictions.