Large Language Models
Showing 27 of 27 projects
A platform to run, manage, and serve open-source large language models (LLMs) locally or on your own infrastructure.
A platform to run, manage, and serve open-source large language models locally with a simple CLI and REST API.
Run large language models (LLMs) privately on everyday desktops and laptops without requiring API calls or GPUs.
A comprehensive open-source guide covering prompt engineering techniques, papers, notebooks, and resources for LLMs, RAG, and AI agents.
A web UI and optimization library for running and fine-tuning open-source AI models locally with 2x faster training and 70% less VRAM.
A unified deep learning system for efficient large-scale model training and inference with advanced parallelism strategies.
A cross-platform desktop client for ChatGPT, Claude, and other LLMs with local data storage and a powerful prompt library.
An open platform for training, serving, and evaluating large language model based chatbots.
An open-source framework for financial large language models, enabling cost-effective fine-tuning for tasks like sentiment analysis and forecasting.
A low-level tensor library for machine learning with integer quantization, automatic differentiation, and zero runtime allocations.
A curated list of open-source large language models licensed for commercial use, including models for general text and code generation.
A curated list of modern Generative AI projects, services, models, and resources across text, image, video, audio, and coding.
Deep Lake is a multimodal data lake and vector store optimized for AI, enabling scalable data management, retrieval, and training for LLM and deep learning applications.
A collection of libraries to optimize AI model performance through inference, infrastructure, and fine-tuning techniques.
A collection of libraries to optimize AI model performance through inference acceleration, infrastructure efficiency, and fine-tuning optimization.
An open-source AI agent platform for financial analysis, automating equity research, algorithmic trading, and risk assessment using LLMs.
A curated list of awesome resources for applying LLMs and deep learning to financial market analysis and algorithmic trading.
An open-source pipeline for training medical domain GPT models using PT, SFT, RLHF, DPO, ORPO, and GRPO methods.
A curated collection of resources for building, training, serving, and optimizing production-grade Large Language Model applications.
An open-source, locally-runnable code completion engine using large language models that works on CPU.
Seamlessly integrate large language models like ChatGPT into scikit-learn for enhanced text analysis tasks.
An open-source framework for building multimodal AI systems that enable large language models to understand and chat about videos and images.
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
A git prepare-commit-msg hook that automatically generates commit messages using OpenAI language models.
An experimental toolkit that automatically generates and maintains codebase documentation using LLMs like GPT-4.
A high-performance, scalable LLM library and reference implementation written in pure Python/JAX for training on TPUs and GPUs.
A neuro-symbolic Python framework that combines classical programming with LLMs through composable primitives and design-by-contract validation.
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