# Datasets

ScholarQABench is a comprehensive evaluation platform designed to assess large language models (LLMs) in assisting researchers with the synthesis of scientific literature. Originating from the OpenScholar project, it offers a comprehensive evaluation framework comprising various datasets and evaluation scripts to measure models' performances across different scientific domains. The platform's significance lies in its ability to aid researchers and developers in understanding and enhancing the practicality and accuracy of language models in scientific literature research.

Tülu 3 is a series of open-source advanced language models that have been fine-tuned to adapt to various tasks and user needs. These models achieve complex training processes by combining elements of proprietary methods, innovative technology, and established academic research. The success of Tülu 3 is rooted in meticulous data management, rigorous experimentation, innovative methodologies, and enhanced training infrastructure. By openly sharing data, recipes, and findings, Tülu 3 aims to empower the community to explore new and innovative fine-tuning techniques.

GenXD is a framework focused on 3D and 4D scene generation, utilizing common camera and object motion found in everyday life to jointly study general 3D and 4D generation. Due to a lack of large-scale 4D data in the community, GenXD initially proposes a data planning process to extract camera poses and object motion intensity from videos. Based on this process, GenXD introduces a large-scale real-world 4D scene dataset: CamVid-30K. By leveraging all 3D and 4D data, the GenXD framework can generate any 3D or 4D scene. It offers a multi-view-time module that separates camera and object motion, learning seamlessly from 3D and 4D data. Furthermore, GenXD employs masked latent conditions to support various conditional views. GenXD can generate videos that follow camera trajectories and consistent 3D views that can be enhanced to 3D representations. It has undergone extensive evaluation across various real-world and synthetic datasets, demonstrating its effectiveness and versatility in 3D and 4D generation compared to previous methods.

Sparsh is a series of general tactile representations trained through self-supervised algorithms such as MAE, DINO, and JEPA. It can generate useful representations for DIGIT, Gelsight'17, and Gelsight Mini, significantly outperforming end-to-end models on downstream tasks proposed by TacBench while supporting data-efficient training for new downstream tasks. The Sparsh project includes PyTorch implementations, pre-trained models, and datasets released alongside Sparsh.

The 1X World Model is a machine learning program that simulates how the world reacts to robotic actions. Leveraging advancements in video generation and autonomous vehicle world modeling technology, it provides a virtual simulator for robots, enabling them to predict future scenarios and evaluate strategies. This model adeptly handles complex object interactions such as rigid bodies, the effects of falling objects, partial observability, deformable objects, and articulated bodies, and conducts evaluations in changing environments critical for robotics development.

LMSYS Org is an organization dedicated to democratizing the technology of large models and their underlying systems infrastructure. They developed the Vicuna chatbot, which rivals GPT-4 in scale (7B/13B/33B) and achieves 90% of ChatGPT's quality. They also offer Chatbot Arena, a platform for large-scale, gamified evaluation of LLMs using crowdsourcing and Elo rating systems. SGLang provides an efficient interface and runtime environment for complex LLM programs. LMSYS-Chat-1M is a large-scale real-world LLM dialogue dataset. FastChat is an open platform for training, serving, and evaluating LLM-based chatbots. MT-Bench is a suite of challenging, multi-turn, open-ended questions designed to evaluate chatbots.

Maintained by the FreedomIntelligence organization, the Apollo project aims to democratize medical AI by providing large language models (LLMs) for the medical domain in multiple languages, covering 600 million people. The project includes models, datasets, benchmarks, and related code.

Process Street is a user-friendly, no-code workflow platform that empowers businesses to create, track, automate, and complete tasks, optimizing processes and enhancing efficiency. Its core features include task assignment, approvals, conditional logic, automation, scheduling, and grouping. Levering AI technology, Process Street also offers AI-driven workflow design that adapts to unique business operations, driving productivity and growth. Furthermore, Process Street provides features like forms, datasets, and pages, along with integrations with popular tools such as Salesforce, Slack, Microsoft Teams, and Google Sheets.

ClearCypherAI is an American AI startup dedicated to building cutting-edge solutions. Our products include Text-to-Speech (T2A), Speech-to-Text (A2T), and Voice-to-Voice (A2A) capabilities, supporting multilingual, multimodal, and real-time voice intelligence. We also offer natural language datasets, threat assessments, and AI customization platforms. Our products boast high customization, advanced technology, and excellent customer support.

Riku.AI is a no-code AI building tool that allows you to create AI models and datasets. Easily leverage AI with integrations to existing tools, APIs, or public shareable links. Making AI accessible to everyone.