Best 150 Model Training and Deployment Tools of 2025

Search-R1 is a reinforcement learning framework designed to train large language models (LLMs) capable of reasoning and calling search engines. Built upon veRL, it supports various reinforcement learning methods and different LLM architectures, enabling efficiency and scalability in tool-augmented reasoning research and development.

GenPRM is an emerging process reward model (PRM) that improves computational efficiency during testing through generative reasoning. This technology provides more accurate reward assessments when handling complex tasks and is suitable for applications in various machine learning and artificial intelligence fields. Its main advantages are the ability to optimize model performance with limited resources and reduce computational costs in practical applications.

Arthur Engine is a tool designed to monitor and govern AI/ML workloads, leveraging popular open-source technologies and frameworks. The enterprise version of this product offers enhanced performance and additional features such as customized enterprise-grade safeguards and metrics, aiming to maximize AI's potential for organizations. It effectively evaluates and optimizes models, ensuring data security and compliance.

Factorio Learning Environment (FLE) is a novel framework built on the game Factorio, used to evaluate the capabilities of large language models (LLMs) in long-term planning, program synthesis, and resource optimization. As LLMs gradually saturate existing benchmark tests, FLE provides a new open-ended evaluation approach. Its importance lies in enabling researchers to gain a more comprehensive and in-depth understanding of the strengths and weaknesses of LLMs. Key advantages include open-ended challenges with exponentially increasing difficulty, and two evaluation protocols: structured tasks and open-ended tasks. This project was developed by Jack Hopkins et al., released as open source, free to use, and aims to promote research by AI researchers on the capabilities of agents in complex, open-ended domains.

Light-R1 is an open-source project developed by Qihoo360, aiming to train long-chain reasoning models through curriculum-style supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL). This project achieves long-chain reasoning capabilities from scratch through decontaminated datasets and efficient training methods. Its main advantages include open-source training data, low-cost training, and excellent performance in mathematical reasoning. The project background is based on the current training needs of long-chain reasoning models, aiming to provide a transparent and reproducible training method. The project is currently free and open-source, suitable for research institutions and developers.

Awesome-LLM-Post-training is a repository focusing on large language model (LLM) post-training methods. It provides in-depth research on LLM post-training, including tutorials, surveys, and guides. This repository is based on the paper "LLM Post-Training: A Deep Dive into Reasoning Large Language Models" and aims to help researchers and developers better understand and apply LLM post-training techniques. This repository is freely available and suitable for both academic research and industrial applications.

Flux is a high-performance communication overlap library developed by ByteDance, designed for tensor and expert parallelism on GPUs. Through efficient kernels and compatibility with PyTorch, it supports various parallelization strategies and is suitable for large-scale model training and inference. Flux's main advantages include high performance, ease of integration, and support for multiple NVIDIA GPU architectures. It excels in large-scale distributed training, particularly with Mixture-of-Experts (MoE) models, significantly improving computational efficiency.

The LLMs.txt generator is an online tool powered by Firecrawl, designed to help users generate integrated text files for LLM training and inference from websites. By integrating web content, it provides high-quality text data for training large language models, thereby improving model performance and accuracy. The main advantages of this tool are its simple operation and high efficiency, allowing for the quick generation of required text files. It is primarily aimed at developers and researchers who need a large amount of text data for model training, providing them with a convenient solution.

Atom of Thoughts (AoT) is a novel reasoning framework that transforms the reasoning process into a Markov process by representing solutions as a combination of atomic problems. This framework significantly improves the performance of large language models on reasoning tasks through decomposition and contraction mechanisms, while reducing wasted computing resources. AoT can be used as an independent reasoning method or as a plugin for existing test-time augmentation methods, flexibly combining the advantages of different methods. This framework is open-source and implemented in Python, making it suitable for researchers and developers to experiment with and apply in the fields of natural language processing and large language models.

AI21-Jamba-Large-1.6 is a hybrid SSM-Transformer architecture base model developed by AI21 Labs, designed for long-text processing and efficient inference. This model demonstrates excellent performance in long-text processing, inference speed, and quality, supports multiple languages, and possesses strong instruction-following capabilities. It is suitable for enterprise-level applications that require processing large amounts of text data, such as financial analysis and content generation. This model is licensed under the Jamba Open Model License, allowing research and commercial use under the license terms.

SRM is a spatial reasoning framework based on a denoising generative model, used for inference tasks on sets of continuous variables. It gradually infers the continuous representation of these variables by assigning an independent noise level to each unobserved variable. This technique excels in handling complex distributions and effectively reduces hallucinations during the generation process. SRM demonstrates for the first time that denoising networks can predict the generation order, thus significantly improving the accuracy of specific inference tasks. The model was developed by the Max Planck Institute for Informatics in Germany and aims to advance research in spatial reasoning and generative models.

The DeepSeek-V3/R1 inference system is a high-performance inference architecture developed by the DeepSeek team, aiming to optimize the inference efficiency of large-scale sparse models. It significantly improves GPU matrix computation efficiency and reduces latency through cross-node expert parallelism (EP) technology. The system employs a double-batch overlapping strategy and a multi-level load balancing mechanism to ensure efficient operation in large-scale distributed environments. Its main advantages include high throughput, low latency, and optimized resource utilization, making it suitable for high-performance computing and AI inference scenarios.

DeepSeek Profile Data is a project focused on performance analysis of deep learning frameworks. It captures performance data of training and inference frameworks through PyTorch Profiler, helping researchers and developers better understand computation and communication overlap strategies and underlying implementation details. This data is crucial for optimizing large-scale distributed training and inference tasks, significantly improving system efficiency and performance. This project is a significant contribution from the DeepSeek team in the field of deep learning infrastructure, aiming to promote the community's exploration of efficient computing strategies.

Expert Parallelism Load Balancer (EPLB) is a load balancing algorithm for Expert Parallelism (EP) in deep learning. It ensures load balance across different GPUs through a redundant expert strategy and a heuristic packing algorithm, while utilizing group-constrained expert routing to reduce inter-node data traffic. This algorithm is significant for large-scale distributed training, improving resource utilization and training efficiency.

DualPipe is an innovative bidirectional pipeline parallel algorithm developed by the DeepSeek-AI team. By optimizing the overlap of computation and communication, this algorithm significantly reduces pipeline bubbles and improves training efficiency. It performs exceptionally well in large-scale distributed training, especially for deep learning tasks requiring efficient parallelization. DualPipe is developed based on PyTorch, easy to integrate and extend, and suitable for developers and researchers who need high-performance computing.

TensorPool is a cloud GPU platform dedicated to simplifying machine learning model training. It provides an intuitive command-line interface (CLI) enabling users to easily describe tasks and automate GPU orchestration and execution. Core TensorPool technology includes intelligent Spot instance recovery, instantly resuming jobs interrupted by preemptible instance termination, combining the cost advantages of Spot instances with the reliability of on-demand instances. Furthermore, TensorPool utilizes real-time multi-cloud analysis to select the cheapest GPU options, ensuring users only pay for actual execution time, eliminating costs associated with idle machines. TensorPool aims to accelerate machine learning engineering by eliminating the extensive cloud provider configuration overhead. It offers personal and enterprise plans; personal plans include a $5 weekly credit, while enterprise plans provide enhanced support and features.

MLGym is an open-source framework and benchmark developed by Meta's GenAI team and the UCSB NLP team for training and evaluating AI research agents. By offering diverse AI research tasks, it fosters the development of reinforcement learning algorithms and helps researchers train and evaluate models in real-world research scenarios. The framework supports various tasks, including computer vision, natural language processing, and reinforcement learning, aiming to provide a standardized testing platform for AI research.

FlexHeadFA is an improved model based on FlashAttention, focusing on providing a fast and memory-efficient accurate attention mechanism. It supports flexible head dimension configuration, significantly enhancing the performance and efficiency of large language models. Key advantages include efficient GPU resource utilization, support for various head dimension configurations, and compatibility with FlashAttention-2 and FlashAttention-3. It is suitable for deep learning scenarios requiring efficient computation and memory optimization, especially excelling in handling long sequences.

FlashMLA is a high-efficiency MLA decoding kernel optimized for Hopper GPUs, specifically designed for variable-length sequence services. Developed using CUDA 12.3 and above, it supports PyTorch 2.0 and above. FlashMLA's primary advantages lie in its efficient memory access and computational performance, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5. This technology is significant for deep learning tasks requiring large-scale parallel computing and efficient memory management, especially in natural language processing and computer vision. Inspired by FlashAttention 2&3 and the cutlass project, FlashMLA aims to provide researchers and developers with a highly efficient computational tool.

MoBA (Mixture of Block Attention) is an innovative attention mechanism specifically designed for large language models dealing with long text contexts. It achieves efficient long sequence processing by dividing the context into blocks and allowing each query token to learn to focus on the most relevant blocks. MoBA's main advantage is its ability to seamlessly switch between full attention and sparse attention, ensuring performance while improving computational efficiency. This technology is suitable for tasks that require processing long texts, such as document analysis and code generation, and can significantly reduce computational costs while maintaining high model performance. The open-source implementation of MoBA provides researchers and developers with a powerful tool, driving the application of large language models in long text processing.

KET-RAG (Knowledge-Enhanced Text Retrieval Augmented Generation) is a powerful retrieval-augmented generation framework enhanced with knowledge graph technology. It achieves efficient knowledge retrieval and generation through a multi-granularity indexing framework, such as a knowledge graph skeleton and a text-keyword bipartite graph. This framework significantly improves retrieval and generation quality while reducing indexing costs, making it well-suited for large-scale RAG applications. Developed in Python, KET-RAG supports flexible configuration and extension, catering to the needs of developers and researchers seeking efficient knowledge retrieval and generation.

One-Shot LoRA is an online platform focused on rapidly training LoRA models from videos. It leverages advanced machine learning technology to efficiently convert video content into LoRA models, providing users with a fast and convenient model generation service. The primary benefits of this product are its ease of use, no login requirement, and privacy protection. It does not require users to upload private data, nor does it store or collect any user information, ensuring the confidentiality and security of user data. This product is mainly aimed at users who need to quickly generate LoRA models, such as designers and developers, helping them quickly obtain the necessary model resources and improve work efficiency.

OLMoE, developed by Ai2, is an open-source language model application aimed at providing researchers and developers a fully open toolkit for conducting AI experiments on-device. The app supports offline operation on iPhone and iPad, ensuring complete privacy for user data. Built upon the efficient OLMoE model, it maintains high performance on mobile devices through optimization and quantization. The open-source nature of the application makes it a vital foundation for the research and development of the next generation of on-device AI applications.

The DeepSeek Model Compatibility Checker is a tool for evaluating whether a device can run different sizes of DeepSeek models. By assessing the device's system memory, video memory, and other configurations alongside the model's parameters and precision requirements, it offers users predictions on model performance. This tool is significant for developers and researchers in selecting the right hardware resources for deploying DeepSeek models, helping them understand device compatibility in advance to avoid operational issues due to insufficient hardware. The DeepSeek model itself is an advanced deep learning model widely used in fields such as natural language processing, known for its efficiency and accuracy. Through this detection tool, users can better leverage the DeepSeek model for their project development and research.

This product consists of a pretraining codebase for large-scale deep recurrent language models, developed in Python. It is optimized for AMD GPU architecture, enabling efficient operation on 4096 AMD GPUs. The core strength of this technology lies in its deep recurrent architecture, which significantly enhances the model's inference capabilities and efficiency. It is primarily aimed at researching and developing high-performance natural language processing models, especially in scenarios requiring large-scale computational resources. The codebase is open-source and licensed under the Apache-2.0 License, making it suitable for academic research and industrial applications.

Steev is a tool specifically designed for AI model training, aimed at simplifying the training process and enhancing model performance. It automatically optimizes training parameters, provides real-time monitoring of the training process, and offers code reviews and suggestions to help users complete model training more efficiently. One of Steev's main advantages is its ease of use, requiring no configuration, making it suitable for engineers and researchers aiming to improve model training efficiency and quality. Currently in a free trial phase, users can experience all its features at no cost.

Kolosal AI is a tool for training and running large language models (LLMs) on local devices. By streamlining the processes of model training, optimization, and deployment, it enables users to leverage AI technology efficiently on local hardware. The tool supports various hardware platforms, provides fast inference speeds, and offers flexible customization capabilities, making it suitable for a wide range of applications from individual developers to large enterprises. Its open-source nature also allows users to conduct secondary development according to their specific needs.

MNN is a deep learning inference engine open-sourced by Alibaba's Taobao technology platform, supporting popular model formats such as TensorFlow, Caffe, and ONNX, while being compatible with commonly used networks like CNN, RNN, and GAN. It achieves exceptional optimization of operator performance and fully supports CPU, GPU, and NPU, maximizing device computing power and widely applied in over 70 AI applications within Alibaba. MNN is recognized for its high performance, ease of use, and versatility, aiming to lower the threshold for AI deployment and promote edge intelligence development.

LLaSA_training is a speech synthesis training project based on LLaMA, aimed at enhancing the efficiency and performance of speech synthesis models by optimizing training and inference computational resources. This project leverages both open-source datasets and proprietary datasets for training, supports various configurations and training methods, and offers high flexibility and scalability. Its main advantages include efficient data processing capabilities, strong speech synthesis effects, and support for multiple languages. This project is suitable for researchers and developers in need of high-performance speech synthesis solutions, applicable to the development of intelligent voice assistants, speech broadcasting systems, and other scenarios.

ASAP (Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills) is an innovative two-stage framework aimed at addressing the dynamic mismatch between simulation and the real world, thereby enabling agile whole-body skills in humanoid robots. This technology significantly enhances a robot's adaptability and coordination in complex dynamic environments by pre-training movement tracking strategies and training a residual motion model with real-world data. Key advantages of ASAP include efficient use of data, substantial performance improvements, and precise control over complex movements, providing new directions for future humanoid robot development, especially in scenarios requiring high flexibility and adaptability.