Best AI tools for< Fine-tune Dpo >

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.

re:tune is a no-code AI app solution that provides everything you need to transform your business with AI, from custom chatbots to autonomous agents. With re:tune, you can build chatbots for any use case, connect any data source, and integrate with all your favorite tools and platforms. re:tune is the missing platform to build your AI apps.

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.

Tune AI is an enterprise Gen AI stack that offers custom models to build competitive advantage. It provides a range of features such as accelerating coding, content creation, indexing patent documents, data audit, automatic speech recognition, and more. The application leverages generative AI to help users solve real-world problems and create custom models on top of industry-leading open source models. With enterprise-grade security and flexible infrastructure, Tune AI caters to developers and enterprises looking to harness the power of AI.

ReplyInbox is a Gmail Chrome extension that revolutionizes email management by harnessing the power of AI. It automates email replies based on your product or service knowledge base, saving you time and effort. Simply select the text you want to respond to, click generate, and let ReplyInbox craft a personalized and high-quality reply. You can also share website links and other documentation with ReplyInbox's AI to facilitate even more accurate and informative responses.

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.

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.

Imajinn AI is a cutting-edge visualization tool that utilizes fine-tuned AI technology to reimagine photos and images into stunning works of art. The platform offers a suite of AI-powered tools for creating personalized children's books, couples portraits, product visualizations, sneaker designs, and more. Users can easily generate concept images, train custom AI models, and access a variety of presets for high-quality outputs. Imajinn AI is designed to empower users to bring their creative ideas to life with ease and efficiency.

Empower is a serverless fine-tuned LLM hosting platform that offers a developer platform for fine-tuned LLMs. It provides prebuilt task-specific base models with GPT4 level response quality, enabling users to save up to 80% on LLM bills with just 5 lines of code change. Empower allows users to own their models, offers cost-effective serving with no compromise on performance, and charges on a per-token basis. The platform is designed to be user-friendly, efficient, and cost-effective for deploying and serving fine-tuned LLMs.

Gretel.ai is a synthetic data platform designed for Generative AI applications. It allows users to generate artificial datasets with the same characteristics as real data, enabling the improvement of AI models without compromising privacy. The platform offers various features such as building synthetic data pipelines, rule-based data transformation, measuring data quality, and customizing language models. Gretel.ai is suitable for industries like finance, healthcare, and the public sector, providing a secure and efficient solution for data generation and model enhancement.

JobHire is an AI-powered job search automation platform that helps users find and apply to relevant job openings. It uses artificial intelligence to analyze and recreate users' resumes, making them more attractive to potential employers. JobHire also automatically creates email addresses and uses them to send responses to suitable vacancies, modifying users' resumes for each specific position. Additionally, it tracks responses from employers and provides users with a dashboard to track their progress.

Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.

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.

Fireworks is a generative AI platform for product innovation. It provides developers with access to the world's leading generative AI models, at the fastest speeds. With Fireworks, developers can build and deploy AI-powered applications quickly and easily.

Polarr Copilots are AI-powered tools that help you create stunning photos, videos, and designs. With Polarr Copilots, you can transform your imagination into reality with just a few simple words. Our Copilots are trained on millions of community-generated edits, so they can create unique and realistic edits that are tailored to your specific needs. Whether you're a professional photographer, a social media enthusiast, or just someone who loves to express themselves through creativity, Polarr Copilots can help you take your creative vision to the next level.

Lucidpic is an AI-powered photo studio that allows users to generate unique, royalty-free, hyper-realistic images of people at a fraction of the cost of running real photoshoots or purchasing stock photography. With Lucidpic, users can create custom characters and people for any scenario, with control over appearance, setting, and style. Lucidpic also offers a variety of features such as AI avatars, stock photos, and customizable features, making it an ideal tool for marketing, design, and creative content.

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.

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.

fal.ai is a generative media platform designed for developers to build the next generation of creativity. It offers lightning-fast inference and access to high-quality generative media models optimized by the fal Inference Engine™. Developers can fine-tune their own models, leverage the fastest AI inference engine for diffusion models, and benefit from the expertise of Fal's head of AI research, Simo Ryu, in implementing LoRAs for diffusion models. The platform provides a world-class developer experience and cost-effective scalability, allowing users to pay only for the computing power they consume.

Gretel.ai is an AI tool that helps users incorporate generative AI into their data by generating synthetic data that is as good or better than the existing data. Users can fine-tune custom AI models and use Gretel's APIs to generate unlimited synthesized datasets, perform privacy-preserving transformations on sensitive data, and identify PII with advanced NLP detection. Gretel's APIs make it simple to generate anonymized and safe synthetic data, allowing users to innovate faster and preserve privacy while doing it. Gretel's platform includes Synthetics, Transform, and Classify APIs that provide users with a complete set of tools to create safe data. Gretel also offers a range of resources, including documentation, tutorials, GitHub projects, and open-source SDKs for developers. Gretel Cloud runners allow users to keep data contained by running Gretel containers in their environment or scaling out workloads to the cloud in seconds. Overall, Gretel.ai is a powerful AI tool for generating synthetic data that can help users unlock innovation and achieve more with safe access to the right data.

Agentq is a tool that utilizes various agentic architectures to complete tasks on the web reliably. It includes a planner-navigator multi-agent architecture, a solo planner-actor agent, an actor-critic multi-agent architecture, and an actor-critic architecture with reinforcement learning and DPO finetuning. The repository also contains an open-source implementation of the research paper 'Agent Q'. Users can set up the tool by installing dependencies, starting Chrome in dev mode, and setting up necessary environment variables. The tool can be run to perform various tasks related to autonomous AI agents.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

Unsloth is a tool that allows users to fine-tune large language models (LLMs) 2-5x faster with 80% less memory. It is a free and open-source tool that can be used to fine-tune LLMs such as Gemma, Mistral, Llama 2-5, TinyLlama, and CodeLlama 34b. Unsloth supports 4-bit and 16-bit QLoRA / LoRA fine-tuning via bitsandbytes. It also supports DPO (Direct Preference Optimization), PPO, and Reward Modelling. Unsloth is compatible with Hugging Face's TRL, Trainer, Seq2SeqTrainer, and Pytorch code. It is also compatible with NVIDIA GPUs since 2018+ (minimum CUDA Capability 7.0).

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

MiniCPM is a series of open-source large models on the client side jointly developed by Face Intelligence and Tsinghua University Natural Language Processing Laboratory. The main language model MiniCPM-2B has only 2.4 billion (2.4B) non-word embedding parameters, with a total of 2.7B parameters. - After SFT, MiniCPM-2B performs similarly to Mistral-7B on public comprehensive evaluation sets (better in Chinese, mathematics, and code capabilities), and outperforms models such as Llama2-13B, MPT-30B, and Falcon-40B overall. - After DPO, MiniCPM-2B also surpasses many representative open-source large models such as Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, and Zephyr-7B-alpha on the current evaluation set MTBench, which is closest to the user experience. - Based on MiniCPM-2B, a multi-modal large model MiniCPM-V 2.0 on the client side is constructed, which achieves the best performance of models below 7B in multiple test benchmarks, and surpasses larger parameter scale models such as Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on the OpenCompass leaderboard. MiniCPM-V 2.0 also demonstrates leading OCR capabilities, approaching Gemini Pro in scene text recognition capabilities. - After Int4 quantization, MiniCPM can be deployed and inferred on mobile phones, with a streaming output speed slightly higher than human speech speed. MiniCPM-V also directly runs through the deployment of multi-modal large models on mobile phones. - A single 1080/2080 can efficiently fine-tune parameters, and a single 3090/4090 can fully fine-tune parameters. A single machine can continuously train MiniCPM, and the secondary development cost is relatively low.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

XTuner is an efficient, flexible, and full-featured toolkit for fine-tuning large models. It supports various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...), VLMs (LLaVA), and various training algorithms (QLoRA, LoRA, full-parameter fine-tune). XTuner also provides tools for chatting with pretrained / fine-tuned LLMs and deploying fine-tuned LLMs with any other framework, such as LMDeploy.

H2O LLM Studio is a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). With H2O LLM Studio, you can easily and effectively fine-tune LLMs without the need for any coding experience. The GUI is specially designed for large language models, and you can finetune any LLM using a large variety of hyperparameters. You can also use recent finetuning techniques such as Low-Rank Adaptation (LoRA) and 8-bit model training with a low memory footprint. Additionally, you can use Reinforcement Learning (RL) to finetune your model (experimental), use advanced evaluation metrics to judge generated answers by the model, track and compare your model performance visually, and easily export your model to the Hugging Face Hub and share it with the community.

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.

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.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

SiLLM is a toolkit that simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework. It provides features such as LLM loading, LoRA training, DPO training, a web app for a seamless chat experience, an API server with OpenAI compatible chat endpoints, and command-line interface (CLI) scripts for chat, server, LoRA fine-tuning, DPO fine-tuning, conversion, and quantization.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

Torchtune is a PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. It provides native-PyTorch implementations of popular LLMs using composable and modular building blocks, easy-to-use and hackable training recipes for popular fine-tuning techniques, YAML configs for easily configuring training, evaluation, quantization, or inference recipes, and built-in support for many popular dataset formats and prompt templates to help you quickly get started with training.

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.

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.

This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.

LLamaTuner is a repository for the Efficient Finetuning of Quantized LLMs project, focusing on building and sharing instruction-following Chinese baichuan-7b/LLaMA/Pythia/GLM model tuning methods. The project enables training on a single Nvidia RTX-2080TI and RTX-3090 for multi-round chatbot training. It utilizes bitsandbytes for quantization and is integrated with Huggingface's PEFT and transformers libraries. The repository supports various models, training approaches, and datasets for supervised fine-tuning, LoRA, QLoRA, and more. It also provides tools for data preprocessing and offers models in the Hugging Face model hub for inference and finetuning. The project is licensed under Apache 2.0 and acknowledges contributions from various open-source contributors.

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.

A witty editor to fine-tune stand-up comedy jokes.

Untangle Your Ethical Knots and 'Fine-Tune' Your Values with JustInGPT

I'm Chic Strategic, your ally in carving out a distinct brand position and fine-tuning your voice. Let's make your brand's presence robust and its message clear in a bustling market.

👉AI绘画，无视版权，精准创作提示词。👈1.可描述画面2.可给出midjourney的绘画提示词3.为每幅画作指定专属 ID，便于精调4.可以画绘制皮克斯拟人可爱动物。1. Can describe the picture . 2. Can give the prompt words for midjourney's painting . 3. Assign a unique ID to each painting to facilitate fine-tuning

An assistant to help you understand transfer learning better

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

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

Expert in refining custom GPT model instructions.

Recommends fine dining restaurants worldwide

A Michelin-starred chef offering French-style plating and recipes.

Boundary Coach is now fine-tuned and ready for use! It's an advanced guide for assertive boundary setting, offering nuanced advice, practical tips, and interactive exercises. It will provide tailored guidance, avoiding medical or legal advice and suggesting professional help when needed.

Enter the world of Secret Somm, where intrigue and fine wine meet. Whether you're a rookie or a connoisseur, your personal wine agent awaits—ready to unveil the secrets of the perfect pour. Your mission, should you choose to accept it, will lead to unparalleled wine discoveries.

Tell us your favourite animal and let us create some fine banknotes for you !

Designed for excellence in Quality Assurance, fine-tuning custom GPT configurations through continuous refinement.

Doing art design and research, including fine arts, audio arts and video arts, designed by Prof. Dr. Fred Y. Ye (Ying Ye)

It acts as an instructor guiding you through music production skills, such as fine-tuning parameters in mixing, mastering, and compression. Additionally, it functions as an aide, offering advice for your music production hurdles with just a screenshot of your production or parameter settings.

Your innovative partner for viral ad copywriting! Dive into viral marketing strategies fine-tuned to your needs!

华体科技灵感的GPT创建与精细调整专家。