Best AI tools for< Model Fine-tuning >

IngestAI is a Silicon Valley-based startup that provides a sophisticated toolbox for data preparation and model selection, powered by proprietary AI algorithms. The company's mission is to make AI accessible and affordable for businesses of all sizes. IngestAI's platform offers a turn-key service tailored for AI builders seeking to optimize AI application development. The company identifies the model best-suited for a customer's needs, ensuring it is designed for high performance and reliability. IngestAI utilizes Deepmark AI, its proprietary software solution, to minimize the time required to identify and deploy the most effective AI solutions. IngestAI also provides data preparation services, transforming raw structured and unstructured data into high-quality, AI-ready formats. This service is meticulously designed to ensure that AI models receive the best possible input, leading to unparalleled performance and accuracy. IngestAI goes beyond mere implementation; the company excels in fine-tuning AI models to ensure that they match the unique nuances of a customer's data and specific demands of their industry. IngestAI rigorously evaluates each AI project, not only ensuring its successful launch but its optimal alignment with a customer's business goals.

Prem is an AI platform that empowers developers and businesses to build and fine-tune generative AI models with ease. It offers a user-friendly development platform for developers to create AI solutions effortlessly. For businesses, Prem provides tailored model fine-tuning and training to meet unique requirements, ensuring data sovereignty and ownership. Trusted by global companies, Prem accelerates the advent of sovereign generative AI by simplifying complex AI tasks and enabling full control over intellectual capital. With a suite of foundational open-source SLMs, Prem supercharges business applications with cutting-edge research and customization options.

Dynamiq is an operating platform for GenAI applications that enables users to build compliant GenAI applications in their own infrastructure. It offers a comprehensive suite of features including rapid prototyping, testing, deployment, observability, and model fine-tuning. The platform helps streamline the development cycle of AI applications and provides tools for workflow automations, knowledge base management, and collaboration. Dynamiq is designed to optimize productivity, reduce AI adoption costs, and empower organizations to establish AI ahead of schedule.

Odin AI is a comprehensive AI platform that offers a range of tools and features to simplify and automate various tasks. It provides solutions for brand compliance, custom templates, guardrails, knowledge graph, model fine-tuning, conversational AI, task automation, meeting note-taking, chatbot building, and more. Odin AI aims to enhance productivity, streamline workflows, and improve decision-making across different industries and use cases.

Algo is a conversational AI chatbot that is different from ChatGPT. Algo is less verbose and more attuned to the user's needs, providing helpful and meaningful insights without a lot of excess chatter. Algo does not use your data for further training and model fine-tuning, and it is designed to keep all communication private and secure. You can delete your data at any time. This provides a higher level of control over personal information compared to ChatGPT, which is a public system and has no provision for data deletion. Beyond its conversational capabilities, Algo boasts built-in features that allow it to browse the web and craft stunning visuals using advanced generative AI models.

poolside is an advanced foundational AI model designed specifically for software engineering challenges. It allows users to fine-tune the model on their own code, enabling it to understand project uniqueness and complexities that generic models can't grasp. The platform aims to empower teams to build better, faster, and happier by providing a personalized AI model that continuously improves. In addition to the AI model for writing code, poolside offers an intuitive editor assistant and an API for developers to leverage.

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.

FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.

Flux LoRA Model Library is an AI tool that provides a platform for finding and using Flux LoRA models suitable for various projects. Users can browse a catalog of popular Flux LoRA models and learn about FLUX models and LoRA (Low-Rank Adaptation) technology. The platform offers resources for fine-tuning models and ensuring responsible use of generated images.

Fine-Tune AI is a tool that allows users to generate fine-tune data sets using prompts. This can be useful for a variety of tasks, such as improving the accuracy of machine learning models or creating new training data for AI applications.

Prompteasy.ai is an AI tool that allows users to fine-tune AI models in less than 5 minutes. It simplifies the process of training AI models on user data, making it as easy as having a conversation. Users can fully customize GPT by fine-tuning it to meet their specific needs. The tool offers data-driven customization, interactive AI coaching, and seamless model enhancement, providing users with a competitive edge and simplifying AI integration into their workflows.

Cradl AI is an AI-powered tool designed to automate document workflows with no-code AI. It enables users to extract data from any document automatically, integrate with no-code tools, and build custom AI models through an easy-to-use interface. The tool empowers automation teams across industries by extracting data from complex document layouts, regardless of language or structure. Cradl AI offers features such as line item extraction, fine-tuning AI models, human-in-the-loop validation, and seamless integration with automation tools. It is trusted by organizations for business-critical document automation, providing enterprise-level features like encrypted transmission, GDPR compliance, secure data handling, and auto-scaling.

Scale AI is an AI tool that accelerates the development of AI applications for various sectors including enterprise, government, and automotive industries. It offers solutions for training models, fine-tuning, generative AI, and model evaluations. Scale Data Engine and GenAI Platform enable users to leverage enterprise data effectively. The platform collaborates with leading AI models and provides high-quality data for public and private sector applications.

OpenLIT is an AI application designed as an Observability tool for GenAI and LLM applications. It empowers model understanding and data visualization through an interactive Learning Interpretability Tool. With OpenTelemetry-native support, it seamlessly integrates into projects, offering features like fine-tuning performance, real-time data streaming, low latency processing, and visualizing data insights. The tool simplifies monitoring with easy installation and light/dark mode options, connecting to popular observability platforms for data export. Committed to OpenTelemetry community standards, OpenLIT provides valuable insights to enhance application performance and reliability.

Infrabase.ai is a directory of AI infrastructure products that helps users discover and explore a wide range of tools for building world-class AI products. The platform offers a comprehensive directory of products in categories such as Vector databases, Prompt engineering, Observability & Analytics, Inference APIs, Frameworks & Stacks, Fine-tuning, Audio, and Agents. Users can find tools for tasks like data storage, model development, performance monitoring, and more, making it a valuable resource for AI projects.

Prompt Octopus is a free tool that allows you to compare multiple prompts side-by-side. You can add as many prompts as you need and view the responses in real-time. This can be helpful for fine-tuning your prompts and getting the best possible results from your AI model.

DocuHelp is an AI-powered platform that enables businesses to effortlessly create professional-grade documents, reports, proposals, and sales pitches in minutes. It facilitates real-time collaboration among team members, ensuring accuracy and efficiency. The tool eliminates the need for email chains and confusion, providing a seamless writing experience. DocuHelp AI is industry-focused, offering backend prompts tailored to specific industries for precise results. Additionally, it allows access to backend systems, enables training models on company data, and provides analytics for fine-tuning based on specific use cases.

Signature is a private artificial intelligence platform that allows enterprises to keep their data secure and leverage AI models trained on their confidential corporate data. The platform offers services for model training, output delivery, and integration of AI capabilities into workflows. Signature aims to optimize generative AI potential for brands and enterprises by providing secure and private AI solutions. The platform also offers consultancy services to assist in AI adoption and content production. With a focus on security, privacy, and customization, Signature helps clients create exclusive and high-performance AI models.

Tensoic AI is an AI tool designed for custom Large Language Models (LLMs) fine-tuning and inference. It offers ultra-fast fine-tuning and inference capabilities for enterprise-grade LLMs, with a focus on use case-specific tasks. The tool is efficient, cost-effective, and easy to use, enabling users to outperform general-purpose LLMs using synthetic data. Tensoic AI generates small, powerful models that can run on consumer-grade hardware, making it ideal for a wide range of applications.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

LLM-SFT is a Chinese large model fine-tuning tool that supports models such as ChatGLM, LlaMA, Bloom, Baichuan-7B, and frameworks like LoRA, QLoRA, DeepSpeed, UI, and TensorboardX. It facilitates tasks like fine-tuning, inference, evaluation, and API integration. The tool provides pre-trained weights for various models and datasets for Chinese language processing. It requires specific versions of libraries like transformers and torch for different functionalities.

SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

DB-GPT-Hub is an experimental project leveraging Large Language Models (LLMs) for Text-to-SQL parsing. It includes stages like data collection, preprocessing, model selection, construction, and fine-tuning of model weights. The project aims to enhance Text-to-SQL capabilities, reduce model training costs, and enable developers to contribute to improving Text-to-SQL accuracy. The ultimate goal is to achieve automated question-answering based on databases, allowing users to execute complex database queries using natural language descriptions. The project has successfully integrated multiple large models and established a comprehensive workflow for data processing, SFT model training, prediction output, and evaluation.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

The Keras-llm-robot Web UI project is an open-source tool designed for offline deployment and testing of various open-source models from the Hugging Face website. It allows users to combine multiple models through configuration to achieve functionalities like multimodal, RAG, Agent, and more. The project consists of three main interfaces: chat interface for language models, configuration interface for loading models, and tools & agent interface for auxiliary models. Users can interact with the language model through text, voice, and image inputs, and the tool supports features like model loading, quantization, fine-tuning, role-playing, code interpretation, speech recognition, image recognition, network search engine, and function calling.

The 'Dive into Large Language Models' series programming practice tutorial is an extension of the 'Artificial Intelligence Security Technology' course lecture notes from Shanghai Jiao Tong University (Instructor: Zhang Zhuosheng). It aims to provide introductory programming references related to large models. Through simple practice, it helps students quickly grasp large models, better engage in course design, or academic research. The tutorial covers topics such as fine-tuning and deployment, prompt learning and thought chains, knowledge editing, model watermarking, jailbreak attacks, multimodal models, large model intelligent agents, and security. Disclaimer: The content is based on contributors' personal experiences, internet data, and accumulated research work, provided for reference only.

This project open sources the **Chinese LLaMA model and the Alpaca large model fine-tuned with instructions**, to further promote the open research of large models in the Chinese NLP community. These models **extend the Chinese vocabulary based on the original LLaMA** and use Chinese data for secondary pre-training, further enhancing the basic Chinese semantic understanding ability. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, significantly improving the model's understanding and execution of instructions.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

LLM Cookbook is a developer-oriented comprehensive guide focusing on LLM for Chinese developers. It covers various aspects from Prompt Engineering to RAG development and model fine-tuning, providing guidance on how to learn and get started with LLM projects in a way suitable for Chinese learners. The project translates and reproduces 11 courses from Professor Andrew Ng's large model series, categorizing them for beginners to systematically learn essential skills and concepts before exploring specific interests. It encourages developers to contribute by replicating unreproduced courses following the format and submitting PRs for review and merging. The project aims to help developers grasp a wide range of skills and concepts related to LLM development, offering both online reading and PDF versions for easy access and learning.

This repository showcases the author's experiments with Large Language Models (LLMs) for text generation tasks. It includes dataset preparation, preprocessing, model fine-tuning using libraries such as Axolotl and HuggingFace, and model evaluation.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

Auto Data is a library designed for the automatic generation of realistic datasets, essential for the fine-tuning of Large Language Models (LLMs). This highly efficient and lightweight library enables the swift and effortless creation of comprehensive datasets across various topics, regardless of their size. It addresses challenges encountered during model fine-tuning due to data scarcity and imbalance, ensuring models are trained with sufficient examples.

The NeMo Framework Launcher is a cloud-native tool designed for launching end-to-end NeMo Framework training jobs. It focuses on foundation model training for generative AI models, supporting large language model pretraining with techniques like model parallelism, tensor, pipeline, sequence, distributed optimizer, mixed precision training, and more. The tool scales to thousands of GPUs and can be used for training LLMs on trillions of tokens. It simplifies launching training jobs on cloud service providers or on-prem clusters, generating submission scripts, organizing job results, and supporting various model operations like fine-tuning, evaluation, export, and deployment.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

CogVideo is an open-source repository that provides pretrained text-to-video models for generating videos based on input text. It includes models like CogVideoX-2B and CogVideo, offering powerful video generation capabilities. The repository offers tools for inference, fine-tuning, and model conversion, along with demos showcasing the model's capabilities through CLI, web UI, and online experiences. CogVideo aims to facilitate the creation of high-quality videos from textual descriptions, catering to a wide range of applications.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

AI Toolkit for Visual Studio Code simplifies generative AI app development by bringing together cutting-edge AI development tools and models from Azure AI Studio Catalog and other catalogs like Hugging Face. Users can browse the AI models catalog, download them locally, fine-tune, test, and deploy them to the cloud. The toolkit offers actions such as finding supported models, testing model inference, fine-tuning models locally or remotely, and deploying fine-tuned models to the cloud. It also provides optimized AI models for Windows and a Q&A section for common issues and resolutions.

Expert in refining custom GPT model instructions.

An assistant to help you understand transfer learning better

大型语言模型的学习和训练指导专家

A witty yet succinct guide for HuggingFace, offering technical assistance on using the platform - based on their Learning Hub

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

Java & Python programming assistant for AI model inference.

Expert in applying mental models across various domains.

Discover A More Robust Business: Craft tailored value proposition statements, develop a comprehensive business model canvas, conduct detailed PESTLE analysis, and gain strategic insights on enhancing business model elements like scalability, cost structure, and market competition strategies. (v1.18)

A software architect for C4 Model generation.

Let's get started by telling me about your business: What do you offer? Who do you serve? ------------------------------------------------------- Need help Prompt Engineering? Reach out on LinkedIn: StephenHnilica

Business Model Canvas Creator - Build and evaluate your business model

Discover if your group, relationship or organization uses specific methods to recruit and maintain control over people

Generates Environmental impact assessment templates based on specific global locations and parameters.

Expert in Marian NMT models and training

Un aiuto a costruire il Business Model Canvas della tua iniziativa

Expert in ML model optimization and tuning!

Business model expert, create detailed reports based on business ideas.

是一个专门用于建立数学模型并撰写python代码的gpt.

Assists with ROMS and COAWST modeling in Linux environments.