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The main features of atla-ai/selene-mini are: General Purpose 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… blinkdl/rwkv-lm — RWKV-LM is a framework for training and deploying recurrent language models. It utilizes a linear-time recurrent… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates… deepseek-ai/deepseek-llm — DeepSeek-LLM is a large language model and causal language model designed for natural language generation. It… ecnu-icalk/educhat — An open-source educational chat model from ICALK, East China Normal University. 开源中英教育对话大模型。(通用基座模型，GPU部署，数据清理) 致敬:… abacaj/mpt-30b-inference — Run inference on the latest MPT-30B model using your CPU. This inference code uses a ggml quantized model. To run the…
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
RWKV-LM is a framework for training and deploying recurrent language models. It utilizes a linear-time recurrent architecture that enables text generation and sequence processing with constant memory and time complexity, avoiding the quadratic scaling of traditional attention caches. The project implements a parallelizable training mechanism that allows recurrent models to be trained using global operations while maintaining cache-free inference. It includes state-tuning capabilities to optimize the initial hidden state and utilizes adaptive probability-mass sampling to control token diversit
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
Run inference on the latest MPT-30B model using your CPU. This inference code uses a ggml quantized model. To run the model we'll use a library called ctransformers that has bindings to ggml in python.