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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
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
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
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
Skywork series models are pre-trained on 3.2TB of high-quality multilingual (mainly Chinese and English) and code data. We have open-sourced the model, training data, evaluation data, evaluation methods, etc.
The main features of skyworkai/skywork are: Foundation Models, General Purpose Models, Text LLM Models.
Projects with overlapping indexed features include: openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… thudm/chatglm3 — ChatGLM3 is an open-weights large language model designed for bilingual conversational interactions in English and… thudm/chatglm2-6b — ChatGLM2-6B is an open-weight large language model designed for natural language conversations and text generation in… thudm/chatglm-6b — ChatGLM-6B is an open-source bilingual large language model designed for natural dialogue and text generation in both… baichuan-inc/baichuan2 — Baichuan2 is a collection of pre-trained large language models, including base and chat variants, designed for natural… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates…