RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology.

A curated and opinionated list of resources for Chief Technology Officers, with the emphasis on startups

Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget)

力扣周赛训练小工具，欢迎使用🎈

该仓库主要记录 大模型（LLMs） 算法工程师相关的面试题

LLMs interview notes and answers:该仓库主要记录大模型（LLMs）算法工程师相关的面试题和参考答案

主要记录大语言大模型（LLMs） 算法（应用）工程师相关的知识及面试题

StoryMaker: Towards consistent characters in text-to-image generation

Awesome-RAG: Collect typical RAG papers and systems.

Asynchronous Low Latency C++ Logging Library

Fast lexical search implementing BM25 in Python using Numpy, Numba and Scipy

Fine-tune LLM agents with online reinforcement learning

[NeurIPS'24] Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models

Minimalistic large language model 3D-parallelism training

Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with …

🧠 世界上覆盖最全的优秀Qwen提示语大全，欢迎贡献你的提示词。🧠 The most comprehensive collection of excellent Qwen prompts in the world. Feel free to contribute your own prompts!

Attribute (or cite) statements generated by LLMs back to in-context information.

🧑‍🚀 全世界最好的LLM资料总结（数据处理、模型训练、模型部署、o1 模型、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.

LMDeploy is a toolkit for compressing, deploying, and serving LLMs.

每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈

A python module to repair invalid JSON, commonly used to parse the output of LLMs

📃 A better UX for chat, writing content, and coding with LLMs.

🐝 GPTSwarm: LLM agents as (Optimizable) Graphs

Awesome In-Context RL: A curated list of In-Context Reinforcement Learning - - —

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

Flight rules for git

BERT score for text generation

Python logging made (stupidly) simple