Best AI tools for< Train Large Reasoning Models >

Ragobble is an audio to LLM data tool that allows you to easily convert audio files into text data that can be used to train large language models (LLMs). With Ragobble, you can quickly and easily create high-quality training data for your LLM projects.

Moreh is an AI platform that aims to make hyperscale AI infrastructure more accessible for scaling any AI model and application. It provides a full-stack infrastructure software from PyTorch to GPUs for the LLM era, enabling users to train large language models efficiently and effectively.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

LAION is a non-profit organization that provides datasets, tools, and models to advance machine learning research. The organization's goal is to promote open public education and encourage the reuse of existing datasets and models to reduce the environmental impact of machine learning research.

Denvr DataWorks AI Cloud is a cloud-based AI platform that provides end-to-end AI solutions for businesses. It offers a range of features including high-performance GPUs, scalable infrastructure, ultra-efficient workflows, and cost efficiency. Denvr DataWorks is an NVIDIA Elite Partner for Compute, and its platform is used by leading AI companies to develop and deploy innovative AI solutions.

Stockpulse is an AI-powered platform that analyzes financial news and communities using Artificial Intelligence. It provides decision support for operations by collecting, filtering, and converting unstructured data into processable information. With extensive coverage of financial media sources globally, Stockpulse offers unique historical data, sentiment analysis, and AI-driven insights for various sectors in the financial markets.

Shaip is a human-powered data processing service specializing in AI and ML models. They offer a wide range of services including data collection, annotation, de-identification, and more. Shaip provides high-quality training data for various AI applications, such as healthcare AI, conversational AI, and computer vision. With over 15 years of expertise, Shaip helps organizations unlock critical information from unstructured data, enabling them to achieve better results in their AI initiatives.

Surge AI is a data labeling platform that provides human-generated data for training and evaluating large language models (LLMs). It offers a global workforce of annotators who can label data in over 40 languages. Surge AI's platform is designed to be easy to use and integrates with popular machine learning tools and frameworks. The company's customers include leading AI companies, research labs, and startups.

Predibase is a platform for fine-tuning and serving Large Language Models (LLMs). It provides a cost-effective and efficient way to train and deploy LLMs for a variety of tasks, including classification, information extraction, customer sentiment analysis, customer support, code generation, and named entity recognition. Predibase is built on proven open-source technology, including LoRAX, Ludwig, and Horovod.

Juice Remote GPU is a software that enables AI and Graphics workloads on remote GPUs. It allows users to offload GPU processing for any CUDA or Vulkan application to a remote host running the Juice agent. The software injects CUDA and Vulkan implementations during runtime, eliminating the need for code changes in the application. Juice supports multiple clients connecting to multiple GPUs and multiple clients sharing a single GPU. It is useful for sharing a single GPU across multiple workstations, allocating GPUs dynamically to CPU-only machines, and simplifying development workflows and deployments. Juice Remote GPU performs within 5% of a local GPU when running in the same datacenter. It supports various APIs, including CUDA, Vulkan, DirectX, and OpenGL, and is compatible with PyTorch and TensorFlow. The team behind Juice Remote GPU consists of engineers from Meta, Intel, and the gaming industry.

Unsloth is an AI tool designed to make finetuning large language models like Llama-3, Mistral, Phi-3, and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy. The tool provides documentation to help users navigate through training their custom models, covering essentials such as installing and updating Unsloth, creating datasets, running, and deploying models. Users can also integrate third-party tools and utilize platforms like Google Colab.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

ONNX Runtime is a production-grade AI engine designed to accelerate machine learning training and inferencing in various technology stacks. It supports multiple languages and platforms, optimizing performance for CPU, GPU, and NPU hardware. ONNX Runtime powers AI in Microsoft products and is widely used in cloud, edge, web, and mobile applications. It also enables large model training and on-device training, offering state-of-the-art models for tasks like image synthesis and text generation.

Cerebras is an AI tool that offers products and services related to AI supercomputers, cloud system processors, and applications for various industries. It provides high-performance computing solutions, including large language models, and caters to sectors such as health, energy, government, scientific computing, and financial services. Cerebras specializes in AI model services, offering state-of-the-art models and training services for tasks like multi-lingual chatbots and DNA sequence prediction. The platform also features the Cerebras Model Zoo, an open-source repository of AI models for developers and researchers.

Cerebras is a leading AI tool and application provider that offers cutting-edge AI supercomputers, model services, and cloud solutions for various industries. The platform specializes in high-performance computing, large language models, and AI model training, catering to sectors such as health, energy, government, and financial services. Cerebras empowers developers and researchers with access to advanced AI models, open-source resources, and innovative hardware and software development kits.

般若AI is an AI-powered platform that provides a variety of AI tools and services to help businesses and individuals with their AI needs. The platform offers a range of features, including AI training, AI consulting, and AI development. 般若AI also has a large community of AI experts who can provide support and guidance to users.

Arcee AI is a platform that offers a cost-effective, secure, end-to-end solution for building and deploying Small Language Models (SLMs). It allows users to merge and train custom language models by leveraging open source models and their own data. The platform is known for its Model Merging technique, which combines the power of pre-trained Large Language Models (LLMs) with user-specific data to create high-performing models across various industries.

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for speed, modularity, and expressiveness, allowing users to define models and optimization through configuration without hard-coding. Caffe supports both CPU and GPU training, making it suitable for research experiments and industry deployment. The framework is extensible, actively developed, and tracks the state-of-the-art in code and models. Caffe is widely used in academic research, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia.

Azure AI Platform by Microsoft offers a comprehensive suite of artificial intelligence services and tools for developers and businesses. It provides a unified platform for building, training, and deploying AI models, as well as integrating AI capabilities into applications. With a focus on generative AI, multimodal models, and large language models, Azure AI empowers users to create innovative AI-driven solutions across various industries. The platform also emphasizes content safety, scalability, and agility in managing AI projects, making it a valuable resource for organizations looking to leverage AI technologies.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

R1-searcher is a tool designed to incentivize the search capability in large reasoning models (LRMs) via reinforcement learning. It enables LRMs to invoke web search and obtain external information during the reasoning process by utilizing a two-stage outcome-supervision reinforcement learning approach. The tool does not require instruction fine-tuning for cold start and is compatible with existing Base LLMs or Chat LLMs. It includes training code, inference code, model checkpoints, and a detailed technical report.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

Recent LLM (Large Language Models)-based CV and multi-modal works.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

OpenManus-RL is an open-source initiative focused on enhancing reasoning and decision-making capabilities of large language models (LLMs) through advanced reinforcement learning (RL)-based agent tuning. The project explores novel algorithmic structures, diverse reasoning paradigms, sophisticated reward strategies, and extensive benchmark environments. It aims to push the boundaries of agent reasoning and tool integration by integrating insights from leading RL tuning frameworks and continuously updating progress in a dynamic, live-streaming fashion.

NExT-GPT is an end-to-end multimodal large language model that can process input and generate output in various combinations of text, image, video, and audio. It leverages existing pre-trained models and diffusion models with end-to-end instruction tuning. The repository contains code, data, and model weights for NExT-GPT, allowing users to work with different modalities and perform tasks like encoding, understanding, reasoning, and generating multimodal content.

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.

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