Best AI tools for< Train Probes >

ChatCube is an AI-powered chatbot maker that allows users to create chatbots for their websites without coding. It uses advanced AI technology to train chatbots on any document or website within 60 seconds. ChatCube offers a range of features, including a user-friendly visual editor, lightning-fast integration, fine-tuning on specific data sources, data encryption and security, and customizable chatbots. By leveraging the power of AI, ChatCube helps businesses improve customer support efficiency and reduce support ticket reductions by up to 28%.

AI Product Shot is an innovative AI tool that enables users to create professional product ads quickly and effortlessly. With AI Product Shot, users can experiment with various product shots, generate photorealistic concepts, and create stunning product ads that drive conversions. The tool eliminates the need for a physical studio setup, allowing brands to bring their products to life with ease. AI Product Shot offers studio-quality results, transforming basic background product shots into professional assets in minutes. Users can train custom AI models, experiment with different environments and lighting, and produce unique product images with unlimited creativity.

Docsumo is an advanced Document AI platform designed for scalability and efficiency. It offers a wide range of capabilities such as pre-processing documents, extracting data, reviewing and analyzing documents. The platform provides features like document classification, touchless processing, ready-to-use AI models, auto-split functionality, and smart table extraction. Docsumo is a leader in intelligent document processing and is trusted by various industries for its accurate data extraction capabilities. The platform enables enterprises to digitize their document processing workflows, reduce manual efforts, and maximize data accuracy through its AI-powered solutions.

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Skillora is an AI Interviewer Tool designed to help individuals practice and improve their interview skills in a safe and realistic environment. Users can take personalized mock interviews with the AI interviewer, receive instant feedback, and access learning resources to enhance their performance. Skillora offers customizable mock interviews tailored to any job description, dynamic follow-up questions, and clear scoring for each response. The application aims to boost users' confidence and success in landing their dream jobs.

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.

CoRover.ai is an AI-powered chatbot designed to help users book train tickets seamlessly through conversation. The chatbot, named AskDISHA, is integrated with the IRCTC platform, allowing users to inquire about train schedules, ticket availability, and make bookings effortlessly. CoRover.ai leverages artificial intelligence to provide personalized assistance and streamline the ticket booking process for users, enhancing their overall experience.

Facet is a cutting-edge generative imagery tool that helps creative professionals focus on what matters. It provides creative assistance without trading off artistic control. Facet helps overcome time and resource constraints that prevent trying out ideas. It offers an intuitive image generation experience with more than just text prompts, including image references, automatic prompt variations, and even custom models trained on the user's exact aesthetic. Facet allows users to train a custom model using their own images in minutes, generating endless assets in their exact vision. Users can add image references to any prompt, instantly getting images that adhere to their subject or style. Facet provides a collaborative canvas for users to riff with teammates and build off of each other's prompts and ideas.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

Chat Data is an AI application that allows users to create custom chatbots using their own data sources. Users can easily build and integrate chatbots with their websites or other platforms, personalize the chatbot's interface, and access advanced features like human support escalation and product updates synchronization. The platform offers HIPAA-compliant medical chat models and ensures data privacy by retaining conversation data exclusively within the user's browser. With Chat Data, users can enhance customer interactions, gather insights, and streamline communication processes.

Stockpulse is an AI-powered platform that analyzes financial news and communities using Artificial Intelligence. It provides decision support for operations by collecting, filtering, and converting unstructured data into processable information. With extensive coverage of financial media sources globally, Stockpulse offers unique historical data, sentiment analysis, and AI-driven insights for various sectors in the financial markets.

Whale is an AI-powered software designed to help businesses document their standard operating procedures, policies, and internal company knowledge. It streamlines the process of onboarding, training, and growing teams by leveraging AI technology to assist in creating and organizing documentation. Whale offers features such as AI-assisted SOP and process documentation, automated training flows, a single source of truth for knowledge management, and an AI assistant named Alice to help with various tasks. The platform aims to systemize and scale businesses by providing a user-friendly interface and dedicated support services.

Scribe is a tool that allows users to create step-by-step guides for any process. It uses AI to automatically generate instructions and screenshots, and it can be used to document processes, train employees, and answer questions. Scribe is available as a Chrome extension and a desktop app.

Shaip is a human-powered data processing service specializing in AI and ML models. They offer a wide range of services including data collection, annotation, de-identification, and more. Shaip provides high-quality training data for various AI applications, such as healthcare AI, conversational AI, and computer vision. With over 15 years of expertise, Shaip helps organizations unlock critical information from unstructured data, enabling them to achieve better results in their AI initiatives.

VJAL Institute is an AI training platform that aims to empower individuals and organizations with the knowledge and skills needed to thrive in the field of artificial intelligence. Through a variety of courses, workshops, and online resources, VJAL Institute provides comprehensive training on AI technologies, applications, and best practices. The platform also offers opportunities for networking, collaboration, and certification, making it a valuable resource for anyone looking to enhance their AI expertise.

ZDNet is a technology news website that provides news, reviews, and advice on the latest innovations in the tech industry. It covers a wide range of topics, including artificial intelligence, cloud computing, digital transformation, energy, robotics, sustainability, transportation, and work life. ZDNet's mission is to help readers understand the latest trends and developments in the tech industry and to make informed decisions about how to use technology to improve their lives and businesses.

SQLAI.ai is a professional SQL multi-tool that leverages AI technology to generate, fix, explain, and optimize SQL queries and databases. It enables users to interact with SQL using everyday language, effortlessly train AI to understand database schemas, and benefit from AI-driven recommendations for query optimization. The platform caters to a wide range of users, from beginners to experts, by simplifying SQL tasks and providing valuable insights for database management. With features like generating SQL data, data analytics, and real-time data insights, SQLAI.ai revolutionizes the way users interact with databases, making SQL tasks simpler, more efficient, and accessible to all.

DataLang is a tool that allows you to chat with your databases, expose a specific set of data (using SQL) to train GPT, and then chat with it in natural language. You can also use DataLang to automatically make your SQL views available via API, share it with your privately users, or make it public.

SQLAI.ai is a powerful SQL multi-tool that utilizes AI to generate, fix, explain, and optimize SQL queries and databases. It empowers users to effortlessly create complex SQL queries using everyday language, optimize queries for better performance, fix syntax errors with ease, and gain a deeper understanding of SQL queries through AI-powered explanations. Additionally, SQLAI.ai enables users to train AI on their database schema, ensuring unparalleled accuracy in AI-generated queries and optimizations.

Tovuti LMS is an adaptive, people-first learning platform that helps organizations create engaging courses, train teams, and track progress. With its easy-to-use interface and powerful features, Tovuti LMS makes learning fun and easy. Tovuti LMS is trusted by leading organizations around the world to provide their employees with the training they need to succeed.

This repository evaluates Large Language Models (LLMs) trained on PGN format chess games using linear probes. It assesses the LLMs' internal understanding of board state and their ability to estimate player skill levels. The repo provides tools to train, evaluate, and visualize linear probes on LLMs trained to play chess with PGN strings. Users can visualize the model's predictions, perform interventions on the model's internal board state, and analyze board state and player skill level accuracy across different LLMs. The experiments in the repo can be conducted with less than 1 GB of VRAM, and training probes on the 8 layer model takes about 10 minutes on an RTX 3050. The repo also includes scripts for performing board state interventions and skill interventions, along with useful links to open-source code, models, datasets, and pretrained models.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

ReST-MCTS is a reinforced self-training approach that integrates process reward guidance with tree search MCTS to collect higher-quality reasoning traces and per-step value for training policy and reward models. It eliminates the need for manual per-step annotation by estimating the probability of steps leading to correct answers. The inferred rewards refine the process reward model and aid in selecting high-quality traces for policy model self-training.

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

Catalyst is a C# Natural Language Processing library designed for speed, inspired by spaCy's design. It provides pre-trained models, support for training word and document embeddings, and flexible entity recognition models. The library is fast, modern, and pure-C#, supporting .NET standard 2.0. It is cross-platform, running on Windows, Linux, macOS, and ARM. Catalyst offers non-destructive tokenization, named entity recognition, part-of-speech tagging, language detection, and efficient binary serialization. It includes pre-built models for language packages and lemmatization. Users can store and load models using streams. Getting started with Catalyst involves installing its NuGet Package and setting the storage to use the online repository. The library supports lazy loading of models from disk or online. Users can take advantage of C# lazy evaluation and native multi-threading support to process documents in parallel. Training a new FastText word2vec embedding model is straightforward, and Catalyst also provides algorithms for fast embedding search and dimensionality reduction.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

Apollo is a multilingual medical LLM that covers English, Chinese, French, Hindi, Spanish, Hindi, and Arabic. It is designed to democratize medical AI to 6B people. Apollo has achieved state-of-the-art results on a variety of medical NLP tasks, including question answering, medical dialogue generation, and medical text classification. Apollo is easy to use and can be integrated into a variety of applications, making it a valuable tool for healthcare professionals and researchers.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

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.

olmOCR is a toolkit designed for training language models to work with PDF documents in real-world scenarios. It includes various components such as a prompting strategy for natural text parsing, an evaluation toolkit for comparing pipeline versions, filtering by language and SEO spam removal, finetuning code for specific models, processing PDFs through a finetuned model, and viewing documents created from PDFs. The toolkit requires a recent NVIDIA GPU with at least 20 GB of RAM and 30GB of free disk space. Users can install dependencies, set up a conda environment, and utilize olmOCR for tasks like converting single or multiple PDFs, viewing extracted text, and running batch inference pipelines.

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

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.

Train-llm-from-scratch is a repository that guides users through training a Large Language Model (LLM) from scratch. The model size can be adjusted based on available computing power. The repository utilizes deepspeed for distributed training and includes detailed explanations of the code and key steps at each stage to facilitate learning. Users can train their own tokenizer or use pre-trained tokenizers like ChatGLM2-6B. The repository provides information on preparing pre-training data, processing training data, and recommended SFT data for fine-tuning. It also references other projects and books related to LLM training.

Amber is the first model in the LLM360 family, an initiative for comprehensive and fully open-sourced LLMs. It is a 7B English language model with the LLaMA architecture. The model type is a language model with the same architecture as LLaMA-7B. It is licensed under Apache 2.0. The resources available include training code, data preparation, metrics, and fully processed Amber pretraining data. The model has been trained on various datasets like Arxiv, Book, C4, Refined-Web, StarCoder, StackExchange, and Wikipedia. The hyperparameters include a total of 6.7B parameters, hidden size of 4096, intermediate size of 11008, 32 attention heads, 32 hidden layers, RMSNorm ε of 1e^-6, max sequence length of 2048, and a vocabulary size of 32000.

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