Open-source alternatives to YAYI

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

BlueLM(蓝心大模型): Open large language models developed by vivo AI Lab

活字通用大模型

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

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

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

XVERSE-13B: A multilingual large language model developed by XVERSE Technology Inc.

YAYI 2 是中科闻歌研发的新一代开源大语言模型，采用了超过 2 万亿 Tokens 的高质量、多语言语料进行预训练。(Repo for YaYi 2 Chinese LLMs)

XVERSE-7B: A multilingual large language model developed by XVERSE Technology Inc.

XVERSE-65B: A multilingual large language model developed by XVERSE Technology Inc.

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

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

A 13B large language model developed by Baichuan Intelligent Technology

Yuan 2.0 Large Language Model

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

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

Baichuan2 is a collection of pre-trained large language models, including base and chat variants, designed for natural language generation and multi-turn conversational AI. It provides an inference engine and a fine-tuning framework to adapt these models to custom datasets and specialized domains. The project features a quantization toolkit and an inference engine that enable model execution across diverse hardware, including graphics processors, central processors, and specialized accelerators. These tools support low-bit weight quantization to reduce memory usage and increase inference spee

百亿参数的中英文双语基座大模型

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 CJK input method framework and configuration set designed for the Rime input engine. It provides a comprehensive system of schemas and dictionary packs to optimize Chinese character entry through pinyin and double-pinyin workflows. The framework is distinguished by its use of Lua-powered extensions that add dynamic utilities, such as inline mathematical calculators, automated timestamps, and text formatting, directly to the input interface. It also features refined word libraries and language models specifically tuned to improve prediction accuracy and first-choice hit rates

Text To Video Synthesis Colab

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

DeepSeek-LLM is a large language model and causal language model designed for natural language generation. It functions as a multi-lingual system capable of predicting the next token in a sequence to perform text completion and conversational generation. The model is specialized for logical reasoning, specifically as a code and math LLM. This enables it to perform complex problem solving, which includes generating executable code and solving mathematical equations through step-by-step analysis. The system's broader capabilities cover conversational AI, including the generation of chat comple

This is the official codebase for scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI.

🤗 TransGPT-7B • 🤗 TransGPT-MM-6B • 🤖 DUOMO • 💬 WeChat

Emu Series: Generative Multimodal Models from BAAI