《大语言模型》作者：赵鑫，李军毅，周昆，唐天一，文继荣

A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.

llmbasedos — Local-First OS Where Your AI Agents Wake Up and Work

Official Code for What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks (In NeurIPS 2023)

[ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following

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[ICLR 2024] Official code for the paper "LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts"

A web-app to explore topics using LLM (less typing and more clicks)

LLM boxing matches

llmBench is a high-depth benchmarking tool designed to measure the raw performance of local LLM runtimes (Ollama, llama.cpp) while providing deep hardware intelligence.

الذكاء الاصطناعي التوليدي باللغة العربية

Like system requirements lab but for LLMs

Various LLM Benchmarks

LLM-powered personal knowledge base. Ingest → Compile → Query → Enhance. Inspired by Karpathy.

大模型API性能指标比较 - 深入分析TTFT、TPS等关键指标

An openAI / LLM API wrapper that simplifies the batching API to reduce costs by 50%

A robust framework to benchmark LLMs specifically for penetration testing use-cases, offering a significant leap forward in the security assessment process.

LLMBind: A Unified Modality-Task Integration Framework

插件概念原型机 /// Agents System/自决策 |类 ChatGpt 插件系统| Gpt-3.5/Gpt-4 |Assistant | 📱 Cross-Platform | 🦾 Async | 🔌 Plugin System

Common interface over multiple LLM providers

Codebase for "How Alignment Shrinks the Generative Horizon"

《大语言模型》综述全书学习笔记

A proof of concept model based on Llama 3.1-8B, featuring reasoning capabilities in Bulgarian.

Making AI Conversations Limitless, for now only for Claude.

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llmbn is a toolkit for generating and refining Bayesian network structures using both large language models (LLMs) and traditional statistical algorithms. It supports flexible, modular workflows for data-free or data-driven structure learning, with easy configuration and sample datasets included.

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LLMB（Large Language Model with Memory）项目旨在构建一个高效、功能丰富的本地大模型记忆系统。它集成了Langchain框架、Ollama语言模型、中文向量模型（text2vec-large-chinese），以及关系型数据库（如SQLite），实现了向量检索和持久化存储功能。该系统能够接收用户的输入，利用内存管理和向量存储来跟踪对话上下文，并通过调用外部的语言模型（Ollama）生成智能回复。此外，LLMB还维护聊天历史数据库，并使用Redis加速某些操作或保持会话状态。