Projects sharing features with Anima

OpenLLM is a framework for deploying, managing, and scaling open-source large language models

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

ChatYuan: Large Language Model for Dialogue in Chinese and English

Chinese-LLaMA 1&2、Chinese-Falcon 基础模型；ChatFlow中文对话模型；中文OpenLLaMA模型；NLP预训练/指令微调数据集

PolyLM is a polyglot large language model, which is aimed to address the following blanks and limitations in current LLM research, offering a comprehensive and innovative solution to advance this field.

Panda项目是于2023年5月启动的开源海外中文大语言模型项目，致力于大模型时代探索整个技术栈，旨在推动中文自然语言处理领域的创新和合作。

DeepSeek-V2 is a large language model designed for natural language processing and the analysis of long text sequences. It utilizes a mixture-of-experts architecture to balance high performance with inference efficiency. The model employs a sparse routing mechanism and shared expert neurons to capture common knowledge while maintaining specialization. It further reduces memory overhead and increases throughput through multi-head latent attention, group-query attention, and low-rank tensor compression. These capabilities enable the processing and retrieval of information from extensive token

The official repo of Aquila2 series proposed by BAAI, including pretrained & chat large language models.

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

中文Mixtral-8x7B（Chinese-Mixtral-8x7B）

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

Text Generation Inference is a production-ready engine designed for the deployment and serving of large language models. It functions as a containerized runtime environment that manages model execution, scales across distributed hardware, and provides high-performance inference capabilities for demanding production environments. The project distinguishes itself through advanced optimization techniques, including continuous batching to maximize hardware utilization and tensor parallelism to shard large models across multiple accelerator cards. It supports efficient inference through custom com

“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.⚠️ This project has been moved to: https://github.com/BayLing-Models/BayLing

Fengshenbang-LM is a Chinese language model ecosystem and pre-training framework designed for the development and fine-tuning of billion-parameter large language models. It serves as a natural language processing pipeline and cross-modal AI platform capable of generating content across different modalities, including text-to-image generation and protein structure prediction. The project provides a domain-specific model adapter for applying pretrained models to specialized industries such as healthcare, finance, and law. It utilizes a distributed configuration system and data sharding to manag

Yuan 2.0 Large Language Model

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

lmdeploy is a high-performance inference engine and deployment framework for large language models and vision models. It functions as a multi-modal model server and compression toolkit designed to serve models with high throughput and low latency. The system enables the distribution of model services across multiple machines using request-based load balancing and tensor parallelism. It includes specialized tools for model quantization and compression to reduce the memory footprint of weights and caches. The framework covers broad capability areas including production deployment, distributed

Mengzi3

骆驼(Luotuo): Open Sourced Chinese Language Models. Developed by 陈启源 @ 华中师范大学 & 李鲁鲁 @ 商汤科技 & 冷子昂 @ 商汤科技

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

TuringMM 智娃AI大模型

Repo for adapting Meta LlaMA2 in Chinese! META最新发布的LlaMA2的汉化版！ （完全开源可商用）

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

The official repo of MiniMax-Text-01 and MiniMax-VL-01, large-language-model & vision-language-model based on Linear Attention

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse