Showing 321-340 of 483 projects
A neurosymbolic perspective on large language models for AI-powered coding and development
A platform for training large historical LLMs
A Python-based desktop AI assistant that integrates with various LLMs and AI tools for coding, task automation, and more.
A benchmark for evaluating the performance of large language models (LLMs) on complex terminal-based tasks.
An open-source React.js agent powered by large language models (LLMs) like GPT-4 for AI-assisted development.
A modular full-stack reinforcement learning library for large language models (LLMs).
A Python library for using large language models (LLMs) as powerful text encoders for downstream NLP tasks.
A TypeScript-based platform that aggregates various LLM APIs into a single hub for easy access and integration.
Automatically generate architecture diagrams from code using large language models (LLMs).
An open-source e-book on building applications with large language models (LLMs), including prompt engineering, architecture design, and more.
Build, enrich, and transform datasets using AI models with no code
A curated list of resources for developing applications using large language models (LLMs) with the Retrieval Augmented Generation (RAG) pattern.
A Python library that provides building blocks for rapid development of generative AI applications.
A C++ project for deploying large language models (LLMs) like ChatGLM and Baichuan using the MNN framework.
A Rust library for running customized and fine-tuned large language models (LLMs) locally or on the edge.
Semantic Reasoning Engine for Large Language Models (LLMs) with AI interpretability and alignment features.
A survey paper on evaluating large language models (LLMs) for developers building AI-powered applications.
A safe reinforcement learning from human feedback (RLHF) system for aligning large language models with human values.
An all-in-one LLMs chat UI for Apple Silicon Macs using the MLX framework, built with Python.
A Python library that provides agent techniques to augment and push the limits of large language models (LLMs).
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