Best AI tools for< Train The Model >

Artifactory is an AI-powered game asset generation tool that helps you create concepts for characters, icons, and backgrounds in seconds. With Artifactory, you can describe your task in text and generate images instantly. You can also use other images as references and train the model according to your style. Artifactory is easy to use and affordable, making it a great option for game developers of all levels.

Custom Vision is a cognitive service provided by Microsoft that offers a user-friendly platform for creating custom computer vision models. Users can easily train the models by providing labeled images, allowing them to tailor the models to their specific needs. The service simplifies the process of implementing visual intelligence into applications, making it accessible even to those without extensive machine learning expertise.

ProJourney AI is a generative AI tool designed for designers and creators. It offers private AI image generation, enabling users to create high-quality images without sharing them publicly. Users can create amazing images starting with a text prompt or by uploading existing images to train the AI model. ProJourney simplifies AI image creation by providing access to Midjourney's generator without Discord, making the process easy and efficient.

RealPhotoAI is an AI-powered tool that allows users to generate unique and lifelike images for various purposes such as creating realistic photos for characters, products, and more. It caters to both personal and business use cases, offering features like visualizing future baby looks, generating dating app photos, creating travel photos, professional profile photos, fitness transformation photos, pet portraits, product visualization, fashion store showcase, and interior design. Users can upload images, train the AI model, describe the desired photo, and receive custom AI-generated images for their projects or applications at an affordable price.

Passarel is an AI tool designed to simplify teammate onboarding by developing bespoke GPT-like models for employee interaction. It centralizes knowledge bases into a custom model, allowing new teammates to access information efficiently. Passarel leverages various integrations to tailor language models to team needs, handling contradictions and providing accurate information. The tool works by training models on chosen knowledge bases, learning from data and configurations provided, and deploying the model for team use.

Stanford HAI is a research institute at Stanford University dedicated to advancing AI research, education, and policy to improve the human condition. The institute brings together researchers from a variety of disciplines to work on a wide range of AI-related projects, including developing new AI algorithms, studying the ethical and societal implications of AI, and creating educational programs to train the next generation of AI leaders. Stanford HAI is committed to developing human-centered AI technologies and applications that benefit all of humanity.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

Tess is the first AI image generator that empowers artists to own their style by creating properly-licensed images. It offers a world-class image editor designed for AI, allowing users to generate art in a consistent visual style. Tess enables artists to create models, edit and customize their generations, and discover how AI can enhance their artistic style. With Tess, users can access copyright-safe generations created by real artists, ensuring ethical AI art practices.

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.

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.

AI Profile Pictures is a website that allows users to generate AI-generated profile pictures. Users can purchase credits to generate 200+ images, and then upload at least 10 photos of themselves (or their subject) for the AI model to train on. Once the photos are uploaded, users can wait 2-3 hours for the AI to generate their profile pictures. Users will have 7 days to generate additional photos if they are not satisfied with their results.

Artiko.ai is a multi-model AI chat platform that integrates advanced AI models such as ChatGPT, Claude 3, Gemini 1.5, and Mistral AI. It offers a convenient and cost-effective solution for work, business, or study by providing a single chat interface to harness the power of multi-model AI. Users can save time and money while achieving better results through features like text rewriting, data conversation, AI assistants, website chatbot, PDF and document chat, translation, brainstorming, and integration with various tools like Woocommerce, Amazon, Salesforce, and more.

Stablematic is a web-based platform that allows users to run Stable Diffusion and other machine learning models without the need for local setup or hardware limitations. It provides a user-friendly interface, pre-installed plugins, and dedicated GPU resources for a seamless and efficient workflow. Users can generate images and videos from text prompts, merge multiple models, train custom models, and access a range of pre-trained models, including Dreambooth and CivitAi models. Stablematic also offers API access for developers and dedicated support for users to explore and utilize the capabilities of Stable Diffusion and other machine learning models.

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.

Kong.ai is an AI-powered platform offering Conversational Chatbots and AI Agents to automate and streamline various business operations such as customer support, sales, HR, and marketing workflows. The platform leverages state-of-the-art language models and machine learning to provide natural and intelligent conversations. Kong.ai provides specialized AI Agents for tasks like lead generation, social media management, recruitment, and more, helping businesses enhance efficiency and productivity.

Rupert AI is an all-in-one AI platform that allows users to train custom AI models for text, audio, video, and images. The platform streamlines AI workflows by providing access to the latest open-source AI models and tools in a single studio tailored to business needs. Users can automate their AI workflow, generate high-quality AI product photography, and utilize popular AI workflows like the AI Fashion Model Generator and Facebook Ad Testing Tool. Rupert AI aims to revolutionize the way businesses leverage AI technology to enhance marketing visuals, streamline operations, and make informed decisions.

OpenAI Platform is a suite of powerful AI tools that can help you build and deploy AI applications. With OpenAI Platform, you can access state-of-the-art AI models, including GPT-3, Codex, and DALL-E 2. You can also use OpenAI Platform to train your own custom AI models. OpenAI Platform is used by businesses of all sizes to build a wide range of AI applications, including chatbots, language translation tools, and image generators.

Incribo is a company that provides synthetic data for training machine learning models. Synthetic data is artificially generated data that is designed to mimic real-world data. This data can be used to train machine learning models without the need for real-world data, which can be expensive and difficult to obtain. Incribo's synthetic data is high quality and affordable, making it a valuable resource for machine learning developers.

Mirage is a custom AI platform that builds custom LLMs to accelerate productivity. It is backed by Sequoia and offers a variety of features, including the ability to create custom AI models, train models on your own data, and deploy models to the cloud or on-premises.

Chatflot is an AI chatbot application that helps businesses automate up to 95% of customer queries. It allows users to create customized AI chatbots based on the ChatGPT language model, enabling them to provide on-demand information to customers through their website. Chatflot is suitable for various industries and offers features like training the chatbot on specific data, optimizing customer interactions, and integrating seamlessly with different CMS platforms. The application aims to enhance customer service, boost sales, and streamline support processes by providing personalized assistance and relevant information to users.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

1.5-Pints is a repository that provides a recipe to pre-train models in 9 days, aiming to create AI assistants comparable to Apple OpenELM and Microsoft Phi. It includes model architecture, training scripts, and utilities for 1.5-Pints and 0.12-Pint developed by Pints.AI. The initiative encourages replication, experimentation, and open-source development of Pint by sharing the model's codebase and architecture. The repository offers installation instructions, dataset preparation scripts, model training guidelines, and tools for model evaluation and usage. Users can also find information on finetuning models, converting lit models to HuggingFace models, and running Direct Preference Optimization (DPO) post-finetuning. Additionally, the repository includes tests to ensure code modifications do not disrupt the existing functionality.

Simple Transformers is a library based on the Transformers library by HuggingFace, allowing users to quickly train and evaluate Transformer models with only 3 lines of code. It supports various tasks such as Information Retrieval, Language Models, Encoder Model Training, Sequence Classification, Token Classification, Question Answering, Language Generation, T5 Model, Seq2Seq Tasks, Multi-Modal Classification, and Conversational AI.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

Steel-LLM is a project to pre-train a large Chinese language model from scratch using over 1T of data to achieve a parameter size of around 1B, similar to TinyLlama. The project aims to share the entire process including data collection, data processing, pre-training framework selection, model design, and open-source all the code. The goal is to enable reproducibility of the work even with limited resources. The name 'Steel' is inspired by a band '万能青年旅店' and signifies the desire to create a strong model despite limited conditions. The project involves continuous data collection of various cultural elements, trivia, lyrics, niche literature, and personal secrets to train the LLM. The ultimate aim is to fill the model with diverse data and leave room for individual input, fostering collaboration among users.

LongRecipe is a tool designed for efficient long context generalization in large language models. It provides a recipe for extending the context window of language models while maintaining their original capabilities. The tool includes data preprocessing steps, model training stages, and a process for merging fine-tuned models to enhance foundational capabilities. Users can follow the provided commands and scripts to preprocess data, train models in multiple stages, and merge models effectively.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

LotteryAi is a lottery prediction artificial intelligence that uses machine learning to predict the winning numbers of any lottery game. It requires Python 3.x and specific libraries like numpy, tensorflow, keras, and art for installation. Users need a data file with past lottery results in a comma-separated format to train the model and generate predictions. The tool comes with no guarantee of accuracy in predicting lottery numbers and is meant for educational and research purposes only.

ChatSim is a tool designed for editable scene simulation for autonomous driving via LLM-Agent collaboration. It provides functionalities for setting up the environment, installing necessary dependencies like McNeRF and Inpainting tools, and preparing data for simulation. Users can train models, simulate scenes, and track trajectories for smoother and more realistic results. The tool integrates with Blender software and offers options for training McNeRF models and McLight's skydome estimation network. It also includes a trajectory tracking module for improved trajectory tracking. ChatSim aims to facilitate the simulation of autonomous driving scenarios with collaborative LLM-Agents.

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

TRACE is a temporal grounding video model that utilizes causal event modeling to capture videos' inherent structure. It presents a task-interleaved video LLM model tailored for sequential encoding/decoding of timestamps, salient scores, and textual captions. The project includes various model checkpoints for different stages and fine-tuning on specific datasets. It provides evaluation codes for different tasks like VTG, MVBench, and VideoMME. The repository also offers annotation files and links to raw videos preparation projects. Users can train the model on different tasks and evaluate the performance based on metrics like CIDER, METEOR, SODA_c, F1, mAP, Hit@1, etc. TRACE has been enhanced with trace-retrieval and trace-uni models, showing improved performance on dense video captioning and general video understanding tasks.

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.

xGitGuard is an AI-based system developed by Comcast Cybersecurity Research and Development team to detect secrets (e.g., API tokens, usernames, passwords) exposed on GitHub repositories. It uses advanced Natural Language Processing to detect secrets at scale and with appropriate velocity. The tool provides workflows for detecting credentials and keys/tokens in both enterprise and public GitHub accounts. Users can set up search patterns, configure API access, run detections with or without ML filters, and train ML models for improved detection accuracy. xGitGuard also supports custom keyword scans for targeted organizations or repositories. The tool is licensed under Apache 2.0.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

This repository contains the code for the paper 'LESS: Selecting Influential Data for Targeted Instruction Tuning'. The work proposes a data selection method to choose influential data for inducing a target capability. It includes steps for warmup training, building the gradient datastore, selecting data for a task, and training with the selected data. The repository provides tools for data preparation, data selection pipeline, and evaluation of the model trained on the selected data.

This repository contains an unofficial implementation of the xLSTM model introduced in Beck et al. (2024). It serves as a didactic tool to explain the details of a modern Long-Short Term Memory model with competitive performance against Transformers or State-Space models. The repository also includes a Lightning-based implementation of a basic LLM for multi-GPU training. It provides modules for scalar-LSTM and matrix-LSTM, as well as an xLSTM LLM built using Pytorch Lightning for easy training on multi-GPUs.

This repository contains the training and inference code needed to replicate the winning solution to the AI Mathematical Olympiad - Progress Prize 1. It consists of fine-tuning DeepSeekMath-Base 7B, high-quality training datasets, a self-consistency decoding algorithm, and carefully chosen validation sets. The training methodology involves Chain of Thought (CoT) and Tool Integrated Reasoning (TIR) training stages. Two datasets, NuminaMath-CoT and NuminaMath-TIR, were used to fine-tune the models. The models were trained using open-source libraries like TRL, PyTorch, vLLM, and DeepSpeed. Post-training quantization to 8-bit precision was done to improve performance on Kaggle's T4 GPUs. The project structure includes scripts for training, quantization, and inference, along with necessary installation instructions and hardware/software specifications.

ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It supports training, inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. The framework includes various training technologies and accelerates inference, evaluation, and deployment modules. It offers a Gradio-based Web-UI interface and best practices for easy application of large models. ms-swift supports a wide range of model types, dataset types, hardware support, lightweight training methods, distributed training techniques, quantization training, RLHF training, multi-modal training, interface training, plugin and extension support, inference acceleration engines, model evaluation, and model quantization.

Your purpose is to create the pytorch code to train language models using pytorch

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NeurIPS 2023 Gateway

OCamlは至高の言語

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