[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
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Updated
Jan 16, 2025 - Python
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework. Website: https://swarms.ai
Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models"
Streamlines and simplifies prompt design for both developers and non-technical users with a low code approach.
LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.
AgentLab: An open-source framework for developing, testing, and benchmarking web agents on diverse tasks, designed for scalability and reproducibility.
Repo for paper "Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration"
Official implementation of paper "Cumulative Reasoning With Large Language Models" (https://arxiv.org/abs/2308.04371)
An extension to AUTOMATIC1111 WebUI for stable diffusion which adds a prompt generator
Official implementation of paper "Meta Prompting for AI Systems" (https://arxiv.org/abs/2311.11482)
Official code repository for Sketch-of-Thought (SoT)
A set of utilities for running few-shot prompting experiments on large-language models
**Interspeech 2022** 《SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks》Speech processing with prompting paradigm
[ECCV 2024] API: Attention Prompting on Image for Large Vision-Language Models
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
Python library for the instruction and reliable validation of structured outputs (JSON) of Large Language Models (LLMs) with Ollama and Pydantic. -> Deterministic work with LLMs.
Offical code of the paper Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning.
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