Stars
Convene a private boardroom of named directors who disagree on purpose. Pose your hardest question, dial the temperature from brainstorm to debate, and watch six lenses stress-test your thinking — …
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
A code for Dynamic Planning for LLM-based Graphical User Interface Automation
Enhancing AI Software Engineering with Repository-level Code Graph
DocBench: A Benchmark for Evaluating LLM-based Document Reading Systems
Autonomous Agents (LLMs) research papers. Updated Daily.
Reproduction Code for Paper "Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models"
[NeurIPS 2024] Code and Data Repo for Paper "Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning"
A generative speech model for daily dialogue.
Code and Data Repo for ACL'24 Paper "Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models"
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
Robust recipes to align language models with human and AI preferences
Official repo for "Make Your LLM Fully Utilize the Context"
Code of "Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model"
Official implementation for "GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Models" (stay tuned & more will be updated)
[ACL 2024] Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models
Structured Chemistry Reasoning with Large Language Models
[NeurIPS 2024] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Large World Model -- Modeling Text and Video with Millions Context
《动手学大模型Dive into LLMs》系列编程实践教程