Projects sharing features with WizardLM

Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical reasoning tasks. These models are trained on high-quality programming datasets to interpret natural language prompts and generate functional source code, explain complex logic, repair code defects, and produce technical documentation. The project distinguishes itself through specialized training methodologies that align model behavior with complex programming instructions and mathematical problem-solving. By utilizing chain-of-thought reasoning and instruction-tuned parameter

InternLM is a large language model and a comprehensive suite of weights designed for text generation and complex reasoning. It functions as an inference engine for serving responses, a fine-tuning framework for adjusting model weights, and a platform for building autonomous AI agents. The system is capable of processing long-context input sequences up to one million tokens for document analysis. It employs chain-of-thought reasoning to solve knowledge-intensive tasks by generating intermediate logic steps before producing a final answer. The project covers model weight optimization through s

This project provides an end-to-end framework for adapting large language models to follow user instructions through supervised fine-tuning. It functions as a comprehensive training pipeline that enables the creation of specialized assistant models by minimizing the difference between predicted outputs and target responses within structured instruction datasets. The framework distinguishes itself by integrating synthetic data generation with memory-efficient training techniques. It utilizes powerful language models to iteratively expand small sets of human-written seeds into diverse, high-qua

BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system

UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros

Chinese-Vicuna is a Chinese large language model and instruction-following AI based on the LLaMA architecture. It is specifically designed for natural language understanding and generation in the Chinese language, utilizing an instruction-tuned model to follow complex user prompts across conversations. The project provides a LoRA fine-tuning framework and quantization systems to enable model adaptation and inference on consumer hardware. It implements quantized inference to reduce memory usage on both CPUs and GPUs, supported by a low-level C++ implementation to minimize system resource requi

Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates as a causal language model that predicts the next token in a sequence to generate coherent conversational responses and perform tasks such as brainstorming, classification, and question answering. The project focuses on the development of models using open datasets suitable for commercial application. It enables the creation of instruction-following models by utilizing curated collections of human-generated instruction-response pairs. The repository provides capabilities for

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

This project is an instruction tuning framework and synthetic data generator that uses high-capacity teacher models to produce instruction-following pairs for training smaller student models. It provides datasets and tools for supervised instruction tuning and reinforcement learning from human feedback. The framework specializes in cross-lingual tuning, offering high-quality instruction-following examples in English and Chinese to improve model generalization across different scripts. It includes a reward modeling tool for creating preference datasets and comparative ratings used to train rew

This project is a technical resource and implementation guide for building transformer-based language model architectures and training pipelines from scratch. It focuses on the design of models capable of natural language processing, including the integration of pretrained weights and the creation of foundational model frameworks. The project specifically emphasizes logical reasoning and mathematical problem solving. It provides a framework for optimizing these capabilities through reinforcement learning and the use of automated verifiers to evaluate and reward correct reasoning paths. The r

GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning, and multilingual conversation. It functions as a multimodal system capable of processing high-resolution visual content and as a long-context model designed to analyze documents with a context window of up to one million tokens. The project differentiates itself through a function calling interface that enables AI agent development by connecting the model to external APIs and real-time web browsing. It includes specialized capabilities for generating functional programming cod

Llama 3 is a collection of pretrained, autoregressive transformer-based models designed for natural language generation, reasoning, and complex instruction following. It functions as a generative AI framework that provides the infrastructure for managing model weights, executing neural network inference, and handling computational workloads across diverse knowledge domains. The project distinguishes itself through an integrated AI safety toolkit that employs secondary classification filtering to inspect inputs and outputs, ensuring adherence to usage compliance and safety standards. It suppor

This project serves as a comprehensive, curated directory of resources, tools, and platforms dedicated to the generative artificial intelligence ecosystem. It functions as a central hub for developers and researchers to discover the frameworks, models, and services necessary for building, deploying, and managing intelligent software applications. The directory distinguishes itself by providing a structured index of specialized tooling across several technical domains. It covers the full lifecycle of generative AI, including the development of autonomous agent systems, the implementation of re

Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software development. It provides specialized model types optimized for general code generation, instruction following, and context-aware infilling. The project includes an instruction-tuned programming model for executing technical tasks via natural language prompts and a code infilling model that predicts missing sections based on surrounding source context. A large context code model is also provided to analyze extensive blocks of source code for improved coherence. The system covers capab

Dromedary: towards helpful, ethical and reliable LLMs.

Qwen3-Coder is a specialized large language model designed for software development, technical reasoning, and automated code synthesis. Built on transformer-based sequence modeling, it functions as a multilingual programming assistant capable of generating, completing, and debugging source code across more than one hundred programming languages. The model distinguishes itself through its capacity to process and maintain logical coherence across massive datasets, supporting context windows of up to one million tokens. This allows for repository-scale reasoning, enabling the model to analyze co

GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an

DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes. The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions. The training framework incorporates group relative policy optimiz

ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a high-performance inference engine designed to support conversational AI, enabling developers to build interactive agents capable of multi-turn dialogue, autonomous code execution, and structured tool invocation. The project distinguishes itself through its focus on hardware-agnostic deployment and resource optimization. It supports distributed model parallelism across multiple graphics cards, paged key-value caching for concurrent request processing, and weight quantization t

CodeLlama is a family of large language models derived from the Llama 2 architecture and specialized for producing, completing, and refactoring source code across multiple programming languages. It functions as a code generation model capable of synthesizing source code from natural language descriptions. The project includes specific model variants designed for different programming tasks. This includes instruction-tuned models trained to follow complex natural language directions and code infilling models that predict and insert missing code segments into existing files by analyzing surroun

Qwen3 is a transformer-based large language model designed as a generative AI foundation for understanding, reasoning, and generating human language. It functions as a comprehensive ecosystem for model training, fine-tuning, and production-ready inference, providing the underlying architecture and weights necessary to build diverse artificial intelligence applications. The project distinguishes itself through extensive support for model quantization and distributed inference, enabling efficient execution across a wide range of hardware from consumer-grade devices to scalable cloud infrastruct

Owl is a framework for agentic workflow automation and multi-agent orchestration. It functions as a system for coordinating autonomous large language model agents to decompose and execute complex tasks through shared communication and collaborative planning. The project distinguishes itself through a multi-modal toolset for processing images, audio, and video, alongside a synthetic data generator that produces domain-specific datasets using self-instruct and verifier loops. It further incorporates a retrieval-augmented generation pipeline framework that integrates long-term memory and real-ti

SmolLM is a project dedicated to the development of small language models. It focuses on training and fine-tuning compact models that maintain high performance while utilizing fewer parameters. The project emphasizes efficient AI inference and on-device text generation, aiming to enable the deployment of lightweight models on edge devices with limited memory and processing power. It utilizes synthetic data generation to produce artificial datasets that improve the reasoning and training of these AI systems. The system supports a variety of optimization and training capabilities, including we

PromptWizard is an automated prompt engineering framework designed to evolve natural language instructions for generative tasks. It functions as an in-context learning optimizer and synthetic data generator, using mutation rounds and performance metrics to iteratively refine large language model instructions. The system employs a self-reflective optimization loop that uses model-generated critiques to rewrite prompts. It distinguishes itself through the use of reasoning chain integration and persona-based prompting to steer the tone and professional quality of model responses. The framework

CodeGeeX is an open-source code model and multilingual large language model designed to generate, translate, and complete source code across multiple programming languages. It functions as an AI coding assistant and a cross-lingual code translator that produces executable code and technical documentation. The project enables natural language programming by turning plain English descriptions into functional programs. It also provides the ability to convert source code from one programming language to another while preserving the original logic and functionality. The system covers a range of c

GLM-4 is an open weights large language model designed as a multimodal chat system. It functions as a reasoning-focused and multilingual model capable of processing and generating responses across text and visual data types. The model is distinguished by its function-calling capabilities, allowing it to interface with external tools and APIs to execute tasks and retrieve real-time information. It is optimized for complex logical reasoning, mathematical problem solving, and deep research involving long-form content generation. Broad capabilities include multilingual text generation, the creat