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All Algorithms implemented in Python
FULL Augment Code, Claude Code, Cluely, CodeBuddy, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus Agent Tools, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Trayc…
WebAssembly binding for llama.cpp - Enabling on-browser LLM inference
Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq]
A Django content management system focused on flexibility and user experience
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
A collection of resources for developers using the ArcGIS Maps SDK for JavaScript.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
A multi-agent engine for the UBC Agents
Replace 'hub' with 'ingest' in any GitHub URL to get a prompt-friendly extract of a codebase
Concatenate a directory full of files into a single prompt for use with LLMs
Qwen3-Coder is the code version of Qwen3, the large language model series developed by Qwen team, Alibaba Cloud.
A system that tries to resolve all issues on a github repo with OpenHands.
Official implementation for the paper: "Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering""
Render notebooks like nbviewer, but using Quarto as the renderer
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
DuckDB is an analytical in-process SQL database management system
ArcGIS Pro SDK for Microsoft .NET is the new .NET SDK for the ArcGIS Pro Application.
A framework for few-shot evaluation of language models.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
A collection of guides and examples for the Gemma open models from Google.
Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, Qwen3, Llama 4, DeepSeek-R1, Gemma 3, TTS 2x faster with 70% less VRAM.
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"