Best AI tools for< Fine-tune Efficiency >

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.

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.

Live Portrait Ai Generator is an AI application that transforms static portrait images into lifelike videos using advanced animation technology. Users can effortlessly animate their portraits, fine-tune animations, unleash artistic styles, and make memories move with text, music, and other elements. The tool offers a seamless stitching technology and retargeting capabilities to achieve perfect results. Live Portrait Ai enhances generation quality and generalization ability through a mixed image-video training strategy and network architecture upgrades.

FriendliAI is a generative AI infrastructure company that offers efficient, fast, and reliable generative AI inference solutions for production. Their cutting-edge technologies enable groundbreaking performance improvements, cost savings, and lower latency. FriendliAI provides a platform for building and serving compound AI systems, deploying custom models effortlessly, and monitoring and debugging model performance. The application guarantees consistent results regardless of the model used and offers seamless data integration for real-time knowledge enhancement. With a focus on security, scalability, and performance optimization, FriendliAI empowers businesses to scale with ease.

Ultimate is a customer support automation platform powered by Generative AI. It offers AI-powered chat automation and ticket automation solutions for various industries such as Ecommerce, Financial Services, Travel, Telecommunications, and Healthtech. UltimateGPT is a bot that integrates with help centers to build bots in minutes. The platform provides backend integrations, analytics, and insights to enhance customer support processes and drive business growth.

HOPPR is a medical-grade platform that accelerates the development of tailored applications to enhance clinical care and optimize workflows. The platform streamlines AI development in medical imaging, prioritizes data security and privacy, and offers diverse datasets across modalities, anatomies, demographics, and geographies. HOPPR empowers faster prototyping, hypothesis testing, and scalable applications for research and medical imaging services, addressing the growing crisis in medical imaging by enabling partners to rapidly develop solutions that improve patient care, enhance operational efficiency, and increase clinician satisfaction.

SD3 Medium is an advanced text-to-image model developed by Stability AI. It offers a cutting-edge approach to generating high-quality, photorealistic images based on textual prompts. The model is equipped with 2 billion parameters, ensuring exceptional quality and resource efficiency. SD3 Medium is currently in a research preview phase, primarily catering to educational and creative purposes. Users can access the model through various licensing options and explore its capabilities via the Stability Platform.

Aktana is an AI and mobile intelligence platform designed for the pharmaceutical and life sciences industry. It offers a suite of products and solutions to help companies develop, plan, and execute go-to-market strategies with precision. Aktana's AI-powered tools enable real-time synchronization and refinement of strategies, leading to increased operational efficiency and sales lift for biopharma leaders. The platform provides immediate feedback from the field, reducing costs and increasing revenues for global pharmaceutical companies.

Sylph AI is an AI tool designed to maximize the potential of LLM applications by providing an auto-optimization library and an AI teammate to assist users in navigating complex LLM workflows. The tool aims to streamline the process of model fine-tuning, hyperparameter optimization, and auto-data labeling for LLM projects, ultimately enhancing productivity and efficiency for users.

Archsynth is an AI-powered tool that helps architects and designers convert their sketches into realistic renders in seconds. It uses cutting-edge technology to enhance efficiency and image quality, allowing users to save time and money. With Archsynth, users can transform their ideas into stunning visuals effortlessly, explore multiple variations, and fine-tune their style with prebuilt templates. Trusted by over 14,000 architects, Archsynth is the #1 AI tool for architecture visualization.

QuickMail AI is an AI-powered email assistant that helps users craft professional emails in seconds. It utilizes AI technology to generate full, well-structured emails from brief prompts, saving users time and effort. The tool offers customizable outputs, allowing users to fine-tune emails to match their personal style. With features like AI-powered generation and time-saving efficiency, QuickMail AI is designed to streamline the email writing process and enhance productivity.

Appen is a leading provider of high-quality data for training AI models. The company's end-to-end platform, flexible services, and deep expertise ensure the delivery of high-quality, diverse data that is crucial for building foundation models and enterprise-ready AI applications. Appen has been providing high-quality datasets that power the world's leading AI models for decades. The company's services enable it to prepare data at scale, meeting the demands of even the most ambitious AI projects. Appen also provides enterprises with software to collect, curate, fine-tune, and monitor traditionally human-driven tasks, creating massive efficiencies through a trustworthy, traceable process.

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.

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.

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.

Replicate is an AI tool that allows users to run and fine-tune models, deploy custom models at scale, and generate various types of content such as images, videos, music, and text with just one line of code. It provides access to a wide range of high-quality models contributed by the community, enabling users to explore, fine-tune, and deploy AI models efficiently. Replicate aims to make AI accessible and practical for real-world applications beyond academic research and demos.

TractoAI is an advanced AI platform that offers deep learning solutions for various industries. It provides Batch Inference with no rate limits, DeepSeek offline inference, and helps in training open source AI models. TractoAI simplifies training infrastructure setup, accelerates workflows with GPUs, and automates deployment and scaling for tasks like ML training and big data processing. The platform supports fine-tuning models, sandboxed code execution, and building custom AI models with distributed training launcher. It is developer-friendly, scalable, and efficient, offering a solution library and expert guidance for AI projects.

MakeLogoAI is an AI-powered logo generator that offers a quick and efficient way to create unique and iconic logos for businesses. The platform utilizes artificial intelligence to generate multiple logo ideas customized to the user's needs, providing vector images and color palettes in under an hour. Users can easily design and fine-tune their logos using the intuitive Logo Editor, making logo creation a seamless and hassle-free process.

AutoApply.Jobs is an AI-driven job application platform that streamlines the job search process by automatically matching job opportunities to user profiles, crafting custom resumes and cover letters, and providing personalized job application insights. With features like AI-driven job applications, custom CVs, and personalized cover letters, AutoApply aims to help users get hired faster and more efficiently. The platform offers a user-friendly interface, a dashboard to manage job applications, and a ChatGPT-powered tool for crafting unique resumes and cover letters. Trusted by 25,000 users, AutoApply saves time and enhances job search success by leveraging AI algorithms and creative responses to application questions.

PromptScaper Workspace is an AI tool designed to assist users in generating text using OpenAI's powerful language models. The tool provides a user-friendly interface for interacting with OpenAI's API to generate text based on specified parameters. Users can input prompts and customize various settings to fine-tune the generated text output. PromptScaper Workspace streamlines the process of leveraging advanced AI language models for text generation tasks, making it easier for users to create content efficiently.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Felafax is a framework designed to tune LLaMa3.1 on Google Cloud TPUs for cost efficiency and seamless scaling. It provides a Jupyter notebook for continued-training and fine-tuning open source LLMs using XLA runtime. The goal of Felafax is to simplify running AI workloads on non-NVIDIA hardware such as TPUs, AWS Trainium, AMD GPU, and Intel GPU. It supports various models like LLaMa-3.1 JAX Implementation, LLaMa-3/3.1 PyTorch XLA, and Gemma2 Models optimized for Cloud TPUs with full-precision training support.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

PrefixQuant is an official PyTorch implementation for static quantization that outperforms dynamic quantization in Large Language Models (LLMs) by utilizing prefixed outliers. The tool provides functionalities for quantization, inference, and visualization of activation distributions. Users can fine-tune quantization settings and evaluate pre-quantized models for tasks like PIQA, ARC, Hellaswag, and Winogrande. The approach aims to improve performance and efficiency in LLMs through innovative quantization techniques.

llm-finetuning is a repository that provides a serverless twist to the popular axolotl fine-tuning library using Modal's serverless infrastructure. It allows users to quickly fine-tune any LLM model with state-of-the-art optimizations like Deepspeed ZeRO, LoRA adapters, Flash attention, and Gradient checkpointing. The repository simplifies the fine-tuning process by not exposing all CLI arguments, instead allowing users to specify options in a config file. It supports efficient training and scaling across multiple GPUs, making it suitable for production-ready fine-tuning jobs.

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.

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.

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.

APOLLO is a memory-efficient optimizer designed for large language model (LLM) pre-training and full-parameter fine-tuning. It offers SGD-like memory cost with AdamW-level performance. The optimizer integrates low-rank approximation and optimizer state redundancy reduction to achieve significant memory savings while maintaining or surpassing the performance of Adam(W). Key contributions include structured learning rate updates for LLM training, approximated channel-wise gradient scaling in a low-rank auxiliary space, and minimal-rank tensor-wise gradient scaling. APOLLO aims to optimize memory efficiency during training large language models.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

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. |  |

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.

The GFM-RAG is a graph foundation model-powered pipeline that combines graph neural networks to reason over knowledge graphs and retrieve relevant documents for question answering. It features a knowledge graph index, efficiency in multi-hop reasoning, generalizability to unseen datasets, transferability for fine-tuning, compatibility with agent-based frameworks, and interpretability of reasoning paths. The tool can be used for conducting retrieval and question answering tasks using pre-trained models or fine-tuning on custom datasets.

RankLLM is a suite of prompt-decoders compatible with open source LLMs like Vicuna and Zephyr. It allows users to create custom ranking models for various NLP tasks, such as document reranking, question answering, and summarization. The tool offers a variety of features, including the ability to fine-tune models on custom datasets, use different retrieval methods, and control the context size and variable passages. RankLLM is easy to use and can be integrated into existing NLP pipelines.

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.

Green-Bit-LLM is a Python toolkit designed for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit Language Models (LLMs). It utilizes the Bitorch Engine for efficient operations on low-bit LLMs, enabling high-performance inference on various GPUs and supporting full-parameter fine-tuning using quantized LLMs. The toolkit also provides evaluation tools to validate model performance on benchmark datasets. Green-Bit-LLM is compatible with AutoGPTQ series of 4-bit quantization and compression models.

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.

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.

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

PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.

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.

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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。