Awesome GitHub RepositoriesText LLM Models

ChatGLM-6B is an open-source bilingual large language model designed for natural dialogue and text generation in both English and Chinese. It is structured as a dialogue model capable of tasks such as role-playing and information extraction. The project provides implementations for quantized language models, using low-precision weights to reduce GPU memory requirements for local inference. It also supports parameter-efficient fine-tuning, allowing model behavior to be optimized for specific tasks without requiring full retraining. The model includes capabilities for local execution on GPUs a

This project is a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.

This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de

ChatGLM2-6B is an open-weight large language model designed for natural language conversations and text generation in both English and Chinese. It functions as a bilingual chat model capable of processing and maintaining coherence across text sequences up to 32K tokens. The model is optimized for local deployment through precision quantization, which reduces memory requirements to allow execution on consumer-grade hardware. It supports distributing model weights across multiple graphics cards to handle parameters that exceed the memory of a single device. The project covers capabilities for

ChatGLM3 is an open-weights large language model designed for bilingual conversational interactions in English and Chinese. It functions as a tool-augmented system capable of calling external functions and executing internal code to resolve complex tasks. The model utilizes four-bit quantization to reduce memory requirements, enabling inference on consumer hardware and diverse processing units including GPUs and CPUs. It features an expanded context window for processing and summarizing long documents and includes a supervised fine-tuning pipeline for adapting the model to specialized domains

MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment

ChatRWKV is an open-source frontend and GPU-accelerated inference engine designed for interacting with RWKV recurrent neural network language models. It provides a self-hosted web chat interface and a specialized client for generating human-like text using a linear-complexity architecture. The project utilizes a GPU-accelerated backend that employs custom CUDA kernels and dynamic model format conversion to increase processing speed and reduce memory overhead. It manages conversation history through state-based context management, updating a fixed-size hidden state to maintain a constant memor

MiniCPM is a collection of small language models designed for local, on-device deployment in resource-constrained environments. The project focuses on running dense Transformer models on consumer hardware, including GPUs, CPUs, and Apple Silicon, without requiring custom code forks. The project distinguishes itself through heavy optimization for edge hardware, utilizing quantized weight compression in GGUF and MLX formats to reduce memory overhead. It implements advanced inference techniques such as speculative sampling and radix-tree prefix caching to accelerate generation speed and throughp

LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and inference execution. It provides a toolkit for updating model parameters through full tuning or memory-efficient adapter algorithms, alongside an inference engine for executing tuned models via command-line or web-based interfaces. The framework includes a dedicated alignment suite for supervised tuning and reward model training to refine model behavior. It features a context window extender to increase maximum input lengths and a multimodal framework for building chatbots that

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

Yi is a bilingual language model and foundation model designed for natural language processing, reasoning, and reading comprehension in both English and Chinese. It is built as a transformer-based architecture capable of general purpose text generation and conversational tasks. The model is distinguished by its ability to function as a long context system, processing and analyzing extended input sequences up to 200k tokens. It also supports quantized versions that use low-bit precision to reduce memory footprints, enabling execution on consumer-grade hardware. The project covers a broad rang

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 a Chinese large language model based on the LLaMA architecture. It is an instruction-tuned model optimized for natural language processing and multi-turn conversations in Chinese. The system includes a framework for parameter-efficient fine-tuning using low-rank adaptation and quantization to reduce memory requirements. It also implements retrieval augmented generation for local document question answering and supports long-context processing for sequences up to 64K tokens. The project covers a broad set of capabilities including supervised instruction tuning, reinforce

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

Firefly is a training framework and inference engine for large language models. It functions as a toolkit for pre-training and fine-tuning various open-weight architectures, providing a system for model alignment and parameter-efficient fine-tuning. The project includes utilities for merging adapter weights back into base models to create standalone files. It also provides a model alignment toolkit to format training data according to specific prompt templates, ensuring conversational consistency across different models. The framework supports distributed model training and preference-based

Baichuan-7B is an open-source 7 billion parameter bilingual Transformer model designed for text generation and few-shot learning across Chinese and English. It is built on a large Transformer architecture trained on a bilingual corpus, enabling it to produce coherent text in both languages from a single model. The model incorporates several optimization techniques that distinguish it from standard large language models. It uses rotary position embeddings that can extrapolate to longer sequences than seen during training, allowing context extension beyond the original 4096-token training lengt

DeepSeek-V2 es un modelo de lenguaje de gran tamaño diseñado para el procesamiento de lenguaje natural y el análisis de secuencias de texto extensas. Utiliza una arquitectura de mezcla de expertos (MoE) para equilibrar un alto rendimiento con la eficiencia en la inferencia. El modelo emplea un mecanismo de enrutamiento disperso y neuronas expertas compartidas para capturar conocimiento común mientras mantiene la especialización. Además, reduce la sobrecarga de memoria y aumenta el rendimiento mediante atención latente de múltiples cabezas, atención de consulta de grupo y compresión de tensores de bajo rango. Estas capacidades permiten el procesamiento y la recuperación de información a partir de un gran número de tokens y facilitan un despliegue económico al reducir los costes de hardware y los cuellos de botella de memoria. El sistema es compatible con interfaces API estándar para su integración con toolchains de modelos de lenguaje existentes.

Fengshenbang-LM es un ecosistema de modelos de lenguaje chino y framework de preentrenamiento diseñado para el desarrollo y ajuste fino de modelos de lenguaje grandes de miles de millones de parámetros. Sirve como una tubería de procesamiento de lenguaje natural y plataforma de IA cross-modal capaz de generar contenido a través de diferentes modalidades, incluyendo generación de texto a imagen y predicción de estructuras de proteínas. El proyecto proporciona un adaptador de modelo específico del dominio para aplicar modelos preentrenados a industrias especializadas como la salud, las finanzas y el derecho. Utiliza un sistema de configuración distribuido y fragmentación de datos (data sharding) para gestionar el entrenamiento de modelos a gran escala a través de múltiples nodos de cómputo. El framework cubre una amplia gama de capacidades, incluyendo procesamiento de lenguaje natural en chino, transformación automatizada de texto y generación de contenido multimodal. Además, admite tareas de lenguaje general como traducción, programación y clasificación de texto. Los flujos de trabajo de predicción y ajuste fino (fine-tuning) posteriores se gestionan y ejecutan a través de una interfaz de línea de comandos.

Baichuan2 es una colección de modelos de lenguaje grandes pre-entrenados, incluyendo variantes base y de chat, diseñados para la generación de lenguaje natural y la IA conversacional multi-turno. Proporciona un motor de inferencia y un framework de ajuste fino (fine-tuning) para adaptar estos modelos a datasets personalizados y dominios especializados. El proyecto cuenta con un kit de herramientas de cuantización y un motor de inferencia que permiten la ejecución del modelo en hardware diverso, incluyendo procesadores gráficos, procesadores centrales y aceleradores especializados. Estas herramientas admiten la cuantización de pesos de bajo bit para reducir el uso de memoria y aumentar la velocidad de inferencia en hardware limitado. El sistema cubre una amplia gama de capacidades, incluyendo entrenamiento distribuido en múltiples máquinas, ajuste fino eficiente en parámetros y alineación supervisada para la interacción humana. También incluye utilidades para la conversión de versiones de modelos y proporciona interfaces conversacionales a través de herramientas de línea de comandos o demostraciones basadas en web.

FlagAI is a distributed deep learning framework and platform designed for the end-to-end lifecycle of large-scale foundation models. It provides a toolkit for training, fine-tuning, and deploying large language models and multi-modal systems across multi-node computing clusters. The project features hardware-agnostic compute abstractions to ensure consistent execution across different accelerators. It includes a dedicated library for parameter-efficient fine-tuning, allowing large neural networks to be adapted to specific tasks with minimal parameter updates and reduced computational overhead