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The official PyTorch implementation of Google's Gemma models
GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning, and multilingual conversation. It functions as a multimodal system capable of processing high-resolution visual content and as a long-context model designed to analyze documents with a context window of up to one million tokens. The project differentiates itself through a function calling interface that enables AI agent development by connecting the model to external APIs and real-time web browsing. It includes specialized capabilities for generating functional programming cod
This project is a large language model inference library and framework designed to run models for text generation, problem solving, and coding assistance. It includes a multimodal framework for processing combined image and text inputs and a tool-use implementation that enables the execution of external functions based on model reasoning. The system features a distributed GPU inference engine that spreads large model workloads across multiple graphics processors to increase processing speed and meet memory requirements. It also provides containerized model deployment through pre-packaged imag
This is an asynchronous Swift client library for calling OpenAI’s API across Apple platforms. It provides native access to chat completions, image generation and editing, speech synthesis and transcription, text embeddings, and content moderation through a single interface built on Swift’s async-await concurrency model. The client supports structured output generation by constraining model responses to a provided JSON schema, and enables real-time consumption of generated text through streaming responses delivered as an AsyncSequence. It includes a thread-based conversation model for managing
Mistral Inference is a library for running Mistral large language models on a GPU, generating text from prompts with token streaming. It loads pretrained model weights from local disk or a remote registry into GPU memory, then produces output tokens one by one for real-time display in interactive applications.
The main features of mistralai/mistral-inference are: Large Language Models, Inference Libraries, Weight Loaders, Local Inference Packages, Pretrained Model Loading, GPU Weight Loading, Local LLM Execution, Prompt-Based Text Generation.
Projects with overlapping indexed features include: google/gemma_pytorch — The official PyTorch implementation of Google's Gemma models. zai-org/glm-4 — GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning,… mistralai/mistral-src — This project is a large language model inference library and framework designed to run models for text generation,… macpaw/openai — This is an asynchronous Swift client library for calling OpenAI’s API across Apple platforms. It provides native… cocktailpeanut/dalai — The simplest way to run LLaMA on your local machine. strands-agents/sdk-python — This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified…