Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.5, GPT-OSS, Llama, and more!

ChatGPT CLI is a powerful, multi-provider command-line interface for working with modern LLMs. It supports OpenAI, Azure, Perplexity, LLaMA, and more, with features like streaming, interactive chat, prompt files, image/audio I/O, MCP tool calls, and an experimental agent mode for safe, multi-step automation.

100M parameter lightweight conversational text-to-speech model with breaths, laughter, multi-speaker dialogue, voice cloning, and streaming. Llama-based, on-device.

Chatbot Arena meets multi-modality! Multi-Modality Arena allows you to benchmark vision-language models side-by-side while providing images as inputs. Supports MiniGPT-4, LLaMA-Adapter V2, LLaVA, BLIP-2, and many more!

An example of multi-agent orchestration with llama-index

Visual Med-Alpaca is an open-source, multi-modal foundation model designed specifically for the biomedical domain, built on the LLaMa-7B.

A Chinese medical ChatGPT based on LLaMa, training from large-scale pretrain corpus and multi-turn dialogue dataset.

“百聆”是一个基于LLaMA的语言对齐增强的英语/中文大语言模型，具有优越的英语/中文能力，在多语言和通用任务等多项测试中取得ChatGPT 90%的性能。BayLing is an English/Chinese LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction.

Multi-language Enhanced LLaMA

Full stack advanced chatbot over LlamaIndex.TS documentation with preview feature using Multi-documents-agents, bootstrapped with create-llama

Multimodal AI agent with Llama 3.2: A Streamlit app that processes text, images, PDFs, and PPTs, integrating NIM microservices, Milvus, and Llama-3.2 models.

This project involves using llamaindex Multi Agents concierge system and Qdrant vector database to customize the RAG application with user preferences over LLMs, Embeddings, Search types, and Reranking Models.

🚀 FlexLLama - Lightweight self-hosted tool for running multiple llama.cpp server instances with OpenAI v1 API compatibility and multi-GPU support

Multimodal Instruction Tuning for Llama 3

Kubernetes operator for local LLM inference with llama.cpp, vLLM, and TGI - multi-GPU, autoscaling, air-gapped, production-ready

This is a demo repository for parallel multi-index question answering using streamlit and llama index

Llama-style transformer in PyTorch with multi-node / multi-GPU training. Includes pretraining, fine-tuning, DPO, LoRA, and knowledge distillation. Scripts for dataset mixing and training from scratch.

This script is a chat application that allows users to ask questions about multiple PDF documents implemented with meta's LLAMA2

The official implementation of ImageBind-LLM and Whisper-LLM from the paper "Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech".

Multi-agent AI system using GPT-4o, DeepSeek v3, and Llama 3.3 to detect if CVE vulnerabilities were exploited as zero-days. Analyzes 50 verified CVEs with 85%+ accuracy using forensic evidence extraction, pattern recognition, and threat intelligence.

No description available

Multimodal RAG using LlamaIndex, Qdrant, llama.cpp for document QA with local VisonLLM and embedding models

Implementation of a multi-turn Chain of Thought (CoT) reasoning system, powered by the Llama 3.1 70B model on Groq.

Llama 2 Docker Image supporting AMD64 and ARCH64 architectures

A local-first autonomous multi-agent orchestration engine with recursive reasoning and long-term memory (RAG). Built with Python, Ollama (Llama 3.2), and ChromaDB.

Multi-Task instruction-tuned LLaMA

Adaptive Single/Multi/Mirror LoRA framework with automatic mode switching driven by synaptic stress signal φ(t). Validated on Llama-3.2-1B (Tinker) and GLUE MRPC with full stress-recovery cycle demonstration.

Multi-LLM agent framework with Claude Code-like tools. Use DeepSeek, Claude, GPT, Llama, or any model — same tools, same skills, swap freely. Includes file editing, code search, shell execution, sub-agents, and a production-ready API. The missing SDK between your LLM and your codebase.

A proxy that hosts multiple single-model runners such as LLama.cpp and vLLM

An upgraded llama.cpp GUI (https://github.com/ggml-org) local-first cloud model llama.cpp GUI multi agent command center with RAG, MCP tools, browser automation, voice, and multi-provider orchestration. Demo Here https://llamahub.netlify.app/