Toloka
Empower AI Development and LLM Fine-Tuning
Description
Toloka is a platform designed to enhance Artificial Intelligence (AI) and Machine Learning (ML) development by providing access to expertly crafted data. It specializes in generating datasets for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), leveraging skilled professionals across more than 20 knowledge domains and over 40 languages. The platform ensures scalability and quality through advanced technology and a large global workforce.
Beyond fine-tuning, Toloka supports various stages of the AI development lifecycle, including data collection across multiple formats (text, image, video, audio) and comprehensive data labeling services optimized by combining ML and human expertise. It also offers robust AI model evaluation capabilities, utilizing human-in-the-loop processes and predefined or custom benchmarks. The platform emphasizes data quality, security, and compliance, adhering to standards like ISO 27001, SOC 2, GDPR, and HIPAA.
Key Features
- Expert Data for SFT & RLHF: Access to skilled experts in 20+ domains and 40+ languages.
- Customized Fine-tuning Datasets: Creation of multi-turn, single-turn, and agent-based datasets with explanations.
- RLHF Data Collection: Generation of preference data via output comparisons, pointwise evaluation, and fine-grained feedback.
- AI Model Evaluation: Human-in-the-loop evaluation via API and pre-defined/custom golden benchmarks.
- ML+Human Data Labeling: Optimized pipelines for classification, moderation, search relevance, etc.
- Diverse Data Collection: Collection of human-generated text, image, video, and audio data globally.
- Advanced Quality Control: Features 50+ automated QC methods and 61 platform-level anti-fraud measures.
- Scalable Global Workforce: Leverages a large crowd from 100+ countries.
- Secure & Compliant Infrastructure: ISO 27001/27701 certified, SOC 2, GDPR, CCPA, HIPAA compliant.
Use Cases
- Fine-tuning Large Language Models (LLMs) for specific tasks or domains.
- Training AI models using Reinforcement Learning from Human Feedback (RLHF).
- Evaluating the performance and safety of AI models.
- Generating high-quality training data for machine learning tasks.
- Collecting diverse datasets to improve model generalizability and reduce bias.
- Developing specialized AI models like code-generating LLMs.
- Improving search engine relevance and accuracy.
- Implementing effective content moderation systems.
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