large-language-models
13 repositories
langflow-ai/langflowLangflow is a powerful tool for building and deploying AI-powered agents and workflows.
mlabonne/llm-courseA structured roadmap for mastering LLMs, from math to deployment — Maxime Labonne's free course splits LLM learning into three tracks: fundamentals, building models (LLM Scientist), and shipping applications (LLM Engineer). It bundles roadmaps, Colab notebooks, and tools like LLM AutoEval and LazyMergekit, and ties into his companion book, the LLM Engineer's Handbook.
binary-husky/gpt_academic为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
bojieli/ai-agent-bookAn open-source book on AI Agent design with 109 runnable labs — A free, open 10-chapter book on AI Agent engineering, framed as LLM + context + tools, by Li Bojie. It ships 109 hands-on experiments, PDF/EPUB downloads, an online reader, and translations into 15 languages, aimed at engineers building real agent systems.
FlowiseAI/FlowiseBuild AI Agents, Visually
ray-project/rayRay scales Python and ML workloads from laptop to cluster — Ray pairs a distributed runtime (tasks, actors, objects) with AI libraries for data, training, tuning, RL and serving. It runs anywhere from a laptop to Kubernetes clusters, and now includes LLM serving/inference tooling plus a distributed debugger and dashboard, making it relevant for teams scaling both classic ML and large language model workloads.
google/langextractGoogle's library for grounding LLM extractions in source text — LangExtract uses LLMs like Gemini or OpenAI models to pull structured data from unstructured text, mapping every extraction back to its exact character span in the source. It supports cloud APIs, local Ollama models, and custom providers, with interactive HTML visualization for verifying results on documents like clinical notes or radiology reports.
langfuse/langfuseOpen-source tracing and evals for LLM apps and agents — Langfuse is a self-hostable platform to trace, evaluate, and debug LLM applications, with integrations for LangChain, LlamaIndex, and AutoGen plus a prompt playground and management tools. Backed by Y Combinator, it suits teams needing open, self-hosted observability instead of closed SaaS-only tools.
stanford-oval/stormAn LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
neuml/txtaiA Single Framework Combining Vector Search, Graphs and LLM Agents — txtai bundles vector indexes, graph networks and relational databases into one embeddings database that powers semantic search, RAG and multi-model workflows. Rather than stitching together separate vector DB, graph and LLM tools, developers get a unified foundation for building autonomous agents and search-driven AI applications in Python.
FareedKhan-dev/train-llm-from-scratchA from-scratch LLM pipeline covering pretraining through RLHF — A full PyTorch-only path from raw text to an aligned, reasoning-capable model, with no trl, peft or transformers dependencies. Covers both a classic transformer and a modern architecture (RoPE, RMSNorm, MoE, KV cache), plus hand-written SFT, DPO, GRPO, LoRA and speculative decoding. Aimed at learners wanting to train LLMs from scratch on a single GPU or even CPU.
AI4Finance-Foundation/FinRobotFinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
OmniJev/FlideExact state forking speeds decision model inference 9.5x over vLLM — Flide is inference infrastructure built for Jev-style decision models. Its exact state forking lets it serve the Jev /v1/systemone API up to 9.5 times faster than vLLM and SGLang, while guaranteeing each answer matches what the model would give for that question alone. Useful for teams running LLM-based decision or judging pipelines who need both speed and deterministic output.