Best AI tools for< Fine-tuning Large Language Models >

Avanzai is a workflow automation tool designed for financial services. It utilizes AI agents to transform financial datasets into actionable insights, simplifying financial data analysis. Users can build charts with public data, connect their own data pipelines, and leverage the platform to perform tasks such as macro analysis, instrument screening, and risk analytics. Avanzai offers a comprehensive suite of tools for financial institutions to optimize their portfolios, screen assets, and analyze risks efficiently.

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.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

Sapien.io is a decentralized data foundry that offers data labeling services powered by a decentralized workforce and gamified platform. The platform provides high-quality training data for large language models through a human-in-the-loop labeling process, enabling fine-tuning of datasets to build performant AI models. Sapien combines AI and human intelligence to collect and annotate various data types for any model, offering customized data collection and labeling models across industries.

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.

Airtrain is a no-code compute platform for Large Language Models (LLMs). It provides a user-friendly interface for fine-tuning, evaluating, and deploying custom AI models. Airtrain also offers a marketplace of pre-trained models that can be used for a variety of tasks, such as text generation, translation, and question answering.

hoopsAI is a pioneering technology company committed to empowering retail investors in the stock market. Our cutting-edge platform leverages the immense power of large language models (LLMs) to provide personalized insights. Through continuous fine-tuning, we enhance customization and precision, enabling you to make informed decisions and maximize your understanding of the markets.

LexEdge is an AI-powered legal practice management solution that revolutionizes how legal professionals handle their responsibilities. It offers advanced features like case tracking, client communications, AI chatbot assistance, document automation, task management, and detailed reporting and analytics. LexEdge enhances productivity, accuracy, and client satisfaction by leveraging technologies such as AI, large language models (LLM), retrieval-augmented generation (RAG), fine-tuning, and custom model training. It caters to solo practitioners, small and large law firms, and corporate legal departments, providing tailored solutions to meet their unique needs.

BotX is a no-code AI platform that enables users to automate and deploy generative AI workflows, chatbots, RAGs, and multi-agent solutions. With production-ready AI systems, users can increase productivity, build AI agents and chatbots, automate workflows, create or process documents, and connect models effortlessly. The platform offers a range of models and fine-tuning options, seamless integration with advanced models like ChatGPT, and enterprise-grade results with grounded responses. Users can protect their data with various deployment options, receive dedicated support, and access tailor-made solutions. BotX helps businesses automate tasks, improve efficiency, and achieve significant return on investment.

Unsloth is an AI tool designed to make finetuning large language models like Llama-3, Mistral, Phi-3, and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy. The tool provides documentation to help users navigate through training their custom models, covering essentials such as installing and updating Unsloth, creating datasets, running, and deploying models. Users can also integrate third-party tools and utilize platforms like Google Colab.

Bagel is an AI & Cryptography Research Lab that focuses on making open source AI monetizable by leveraging novel cryptography techniques. Their innovative fine-tuning technology tracks the evolution of AI models, ensuring every contribution is rewarded. Bagel is built for autonomous AIs with large resource requirements and offers permissionless infrastructure for seamless information flow between machines and humans. The lab is dedicated to privacy-preserving machine learning through advanced cryptography schemes.

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.

FineTuneAIs.com is a platform that specializes in custom AI model fine-tuning. Users can fine-tune their AI models to achieve better performance and accuracy. The platform requires JavaScript to be enabled for optimal functionality.

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.

Kiln is an AI tool designed for fine-tuning LLM models, generating synthetic data, and facilitating collaboration on datasets. It offers intuitive desktop apps, zero-code fine-tuning for various models, interactive visual tools for data generation, Git-based version control for datasets, and the ability to generate various prompts from data. Kiln supports a wide range of models and providers, provides an open-source library and API, prioritizes privacy, and allows structured data tasks in JSON format. The tool is free to use and focuses on rapid AI prototyping and dataset collaboration.

Sensay is a platform that specializes in creating digital AI Replicas, offering cutting-edge cloning technology to simplify the process of developing humanlike AI Replicas. These Replicas are designed to preserve and share wisdom, catering to various needs such as dementia care, custom solutions, education, and fan engagement. Sensay ensures the creation of personalized Replicas that mimic individual personalities for realistic interactions, with a focus on continuous learning and enhancing interaction quality over time. The platform also delves into ethical and philosophical implications, emphasizing privacy protection, consent, and the exploration of identity concepts.

Gradient is an AI automation platform designed specifically for enterprise AI purposes. It offers a seamless way to automate manual workflows with minimal effort, providing business intuition and industry expertise. The platform ensures unmatched compliance with various regulations and prioritizes privacy and security. Gradient's Agent Foundry enables users to automate tasks, integrate data, and optimize workflows efficiently, making it a valuable tool for modern enterprises.

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.

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.

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.

This repository contains projects and notes on common practical techniques for fine-tuning Large Language Models (LLMs). It includes fine-tuning LLM notebooks, Colab links, LLM techniques and utils, and other smaller language models. The repository also provides links to YouTube videos explaining the concepts and techniques discussed in the notebooks.

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.

This repository is a comprehensive survey of harmful fine-tuning attacks and defenses for large language models (LLMs). It provides a curated list of must-read papers on the topic, covering various aspects such as alignment stage defenses, fine-tuning stage defenses, post-fine-tuning stage defenses, mechanical studies, benchmarks, and attacks/defenses for federated fine-tuning. The repository aims to keep researchers updated on the latest developments in the field and offers insights into the vulnerabilities and safeguards related to fine-tuning LLMs.

The repository Adaptive-MT-LLM-Fine-tuning contains code and data for the paper 'Fine-tuning Large Language Models for Adaptive Machine Translation'. It focuses on enhancing Mistral 7B, a large language model, for real-time adaptive machine translation in the medical domain. The fine-tuning process involves using zero-shot and one-shot translation prompts to improve terminology and style adherence. The repository includes training and test data, data processing code, fuzzy match retrieval techniques, fine-tuning methods, conversion to CTranslate2 format, tokenizers, translation codes, and evaluation metrics.

LLM-Tuning is a collection of tools and resources for fine-tuning large language models (LLMs). It includes a library of pre-trained LoRA models, a set of tutorials and examples, and a community forum for discussion and support. LLM-Tuning makes it easy to fine-tune LLMs for a variety of tasks, including text classification, question answering, and dialogue generation. With LLM-Tuning, you can quickly and easily improve the performance of your LLMs on downstream tasks.

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).

LLM_QLoRA is a repository for fine-tuning Large Language Models (LLMs) using QLoRA methodology. It provides scripts for training LLMs on custom datasets, pushing models to HuggingFace Hub, and performing inference. Additionally, it includes models trained on HuggingFace Hub, a blog post detailing the QLoRA fine-tuning process, and instructions for converting and quantizing models. The repository also addresses troubleshooting issues related to Python versions and dependencies.

This project provides a comprehensive guide to fine-tuning large language models (LLMs) with efficient methods like LoRA and P-tuning V2. It includes detailed instructions, code examples, and performance benchmarks for various LLMs and fine-tuning techniques. The guide also covers data preparation, evaluation, prediction, and running inference on CPU environments. By leveraging this guide, users can effectively fine-tune LLMs for specific tasks and applications.

This repository provides a comprehensive set of tools for fine-tuning large language models (LLMs) using various techniques, including full parameter training, LoRA (Low-Rank Adaptation), and P-Tuning V2. It supports a wide range of LLM models, including Qwen, Yi, Llama, and others. The repository includes scripts for data preparation, training, and inference, making it easy for users to fine-tune LLMs for specific tasks. Additionally, it offers a collection of pre-trained models and provides detailed documentation and examples to guide users through the process.

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.

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.

Kubeflow Trainer is a Kubernetes-native project for fine-tuning large language models (LLMs) and enabling scalable, distributed training of machine learning (ML) models across various frameworks. It allows integration with ML libraries like HuggingFace, DeepSpeed, or Megatron-LM to orchestrate ML training on Kubernetes. Develop LLMs effortlessly with the Kubeflow Python SDK and build Kubernetes-native Training Runtimes with Kubernetes Custom Resources APIs.

MLX LM is a Python package designed for generating text and fine-tuning large language models on Apple silicon using MLX. It offers integration with the Hugging Face Hub for easy access to thousands of LLMs, support for quantizing and uploading models to the Hub, low-rank and full model fine-tuning capabilities, and distributed inference and fine-tuning with `mx.distributed`. Users can interact with the package through command line options or the Python API, enabling tasks such as text generation, chatting with language models, model conversion, streaming generation, and sampling. MLX LM supports various Hugging Face models and provides tools for efficient scaling to long prompts and generations, including a rotating key-value cache and prompt caching. It requires macOS 15.0 or higher for optimal performance.

A curated list of high-quality papers on resource-efficient Large Language Models (LLMs) with a focus on various aspects such as architecture design, pre-training, fine-tuning, inference, system design, and evaluation metrics. The repository covers topics like efficient transformer architectures, non-transformer architectures, memory efficiency, data efficiency, model compression, dynamic acceleration, deployment optimization, support infrastructure, and other related systems. It also provides detailed information on computation metrics, memory metrics, energy metrics, financial cost metrics, network communication metrics, and other metrics relevant to resource-efficient LLMs. The repository includes benchmarks for evaluating the efficiency of NLP models and references for further reading.

Hands-On-LLM-Applications-Development is a repository focused on developing applications using Large Language Models (LLMs). The repository provides hands-on tutorials, guides, and resources for building various applications such as LangChain for LLM applications, Retrieval Augmented Generation (RAG) with LangChain, building LLM agents with LangGraph, and advanced LangChain with OpenAI. It covers topics like prompt engineering for LLMs, building applications using HuggingFace open-source models, LLM fine-tuning, and advanced RAG applications.

LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM fine-tuning experiments on your data and gathering their results. It allows users to control all elements of a typical experimentation pipeline - prompts, open-source LLMs, optimization strategy, and LLM testing - through a single YAML configuration file. The toolkit supports basic, intermediate, and advanced usage scenarios, enabling users to run custom experiments, conduct ablation studies, and automate fine-tuning workflows. It provides features for data ingestion, model definition, training, inference, quality assurance, and artifact outputs, making it a comprehensive tool for fine-tuning large language models.

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 repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Recent LLM (Large Language Models)-based CV and multi-modal works.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

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

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.

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.

👉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

华体科技灵感的GPT创建与精细调整专家。

Expert in refining custom GPT model instructions.

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

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

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.

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

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

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

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

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