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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of Large Language Model resources, covering model training, serving, fine-tuning, and building LLM applications.
| Date | Stars |
|---|---|
| 2026-07-31 | 4992 |
| 2026-08-01 | 4993 |
| 2026-08-02 | 4993 |
| 2026-08-06 | 4993 |
Today
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growth rate 0.00%/day
# LLM-engineer-handbook
🔥 Large Language Models(LLM) have taken the ~~NLP community~~ ~~AI community~~ **the Whole World** by storm.
Why do we create this repo?
- Everyone can now build an LLM demo in minutes, but it takes a real LLM/AI expert to close the last mile of performance, security, and scalability gaps.
- The LLM space is complicated! This repo provides a curated list to help you navigate so that you are more likely to build production-grade LLM applications. It includes a collection of Large Language Model frameworks and tutorials, covering model training, serving, fine-tuning, LLM applications & prompt optimization, and LLMOps.
*However, classical ML is not going away. Even LLMs need them. We have seen classical models used for protecting data privacy, detecing hallucinations, and more. So, do not forget to study the fundamentals of classical ML.*
## Overview
The current workflow might look like this: You build a demo using an existing application library or directly from LLM model provider SDKs. It works somehow, but you need to further create evaluation and training datasets to optimize the performance (e.g., accuracy, latency, cost).
You can do prompt engineering or auto-prompt optimization; you can create a larger dataset to fine-tune the LLM or use Direct Preference Optimization (DPO) to align the model with human preferences.
Then you need to consider the serving and LLMOps to deploy the model at scale and pipelines to refresh the data.
We organize the resources by (1) tracking all libraries, frameworks, and tools, (2) learning resources on the whole LLM lifecycle, (3) understanding LLMs, (4) social accounts and community, and (5) how to contribute to this repo.
- [LLM-engineer-handbook](#llm-engineer-handbook)
- [Overview](#overview)
- [Libraries \& Frameworks \& Tools](#libraries--frameworks--tools)
- [Applications](#applications)
- [Pretraining](#pretraining)
- [Fine-tuning](#fine-tuning)
- [Serving](#serving)
- [Prompt Management](#prompt-management)
- [Datasets](#datasets)
- [Benchmarks](#benchmarks)
- [Learning Resources for LLMs](#learning-resources-for-llms)
- [Applications](#applications-1)
- [Agent](#agent)
- [Modeling](#modeling)
- [Training](#training)
- [Fine-tuning](#fine-tuning-1)
- [Fundamentals](#fundamentals)
- [Books](#books)
- [Newsletters](#newsletters)
- [Auto-optimization](#auto-optimization)
- [Understanding LLMs](#understanding-llms)
- [Social Accounts \& Community](#social-accounts--community)
- [Social Accounts](#social-accounts)
- [Community](#community)
- [Contributing](#contributing)
# Libraries & Frameworks & Tools
## Applications
**Build & Auto-optimize**
- [AdalFlow](https://github.com/SylphAI-Inc/AdalFlow) - The library to build & auto-optimize LLM applications, from Chatbot, RAG, to Agent by [SylphAI](https://www.sylph.ai/).
- [dspy](https://github.com/stanfordnlp/dspy) - DSPy: The framework for programming—not prompting—foundation models.
**Build**
- [LlamaIndex](https://github.com/jerryjliu/llama_index) — A Python library for augmenting LLM apps with data.
- [LangChain](https://github.com/hwchase17/langchain) — A popular Python/JavaScript library for chaining sequences of language model prompts.
- [Haystack](https://github.com/deepset-ai/haystack) — Python framework that allows you to build applications powered by LLMs.
- [Instill Core](https://github.com/instill-ai/instill-core) — A platform built with Go for orchestrating LLMs to create AI applications.
**Prompt Optimization**
- [AutoPrompt](https://github.com/Eladlev/AutoPrompt) - A framework for prompt tuning using Intent-based Prompt Calibration.
- [PromptFify](https://github.com/promptslab/Promptify) - A library for prompt engineering that simplifies NLP tasks (e.g., NER, classification) using LLMs like GPT.
**Others**
- [LiteLLM](https://github.com/BerriAI/litellm) - Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs inExcerpt of 21,743 characters
Read on GitHub
Li Yin · SylphAI · United States
64
Alex R. · @multi-modal-ai
6
6
Bhimraj Yadav · Nepal
2
huggingface · Belgium
2
Sai Sanjay
2
Paul Iusztin · Decoding AI
2
Jaume Ferrarons · New Work SE (XING) · Spain
1
1
Mahmoud Mabrouk · Agenta.ai · Germany
1
1
abhishek thakur
1
Eigent.AI
1
Alex Strick van Linschoten · ZenML · Netherlands
1
Aftaab Siddiqui · @cachevector
1
Adi Polak · Software Engineer · United States
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:33c2c693695a2881, desc:fine-tuning, desc:fine tuning