Showing 181-200 of 208 projects
A comprehensive survey of efficient large language models for AI and machine learning systems.
A Home Assistant integration and model to control your smart home using a local large language model.
DriveLM is a graph visual question answering model for autonomous driving tasks, built using large language models.
The official implementation of Self-Play Fine-Tuning (SPIN), a deep learning technique for fine-tuning large language models.
A collection of Jupyter notebooks and tutorials on using LangChain and prompt engineering with large language models like ChatGPT.
A family of diffusion models for text-to-audio generation, targeting vibe coders who build with AI tools.
Code for a paper on parameter-efficient fine-tuning of large language models using adapter modules.
A repository that provides daily updates on the latest research papers related to large language models (LLMs).
A curated list of must-read papers on knowledge editing for large language models.
A survey of progress and challenges in using large language models for multi-agent systems.
A curated list of resources on large language models and foundation models for time series, spatiotemporal, and event data analysis.
A generalized information-seeking agent system with Large Language Models (LLMs) for vibe coders.
An open-source speech dialogue generation model that enables expressive dialogue speech synthesis in Chinese and English.
Highly performant, modular, and production-ready inference, ingestion, and indexing library built in Rust for AI-powered applications.
A curated list of foundation models for vision and language tasks, useful for vibe coders building AI-powered applications.
Parallax is a distributed model serving framework that lets you build your own AI cluster anywhere.
A Python library serving multiple LoRA finetuned large language models (LLMs) as one.
A Python-based framework for building large language models for computer vision tasks.
An official implementation of a system for improving video understanding and generation with better captions.
A reading list and survey paper on hallucination in large language models (LLMs) for AI-focused developers.
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