Enterprise-grade, zero-code platform for full-lifecycle model training and optimization—bridging foundation models with industry applications so businesses can quickly build custom AI without specialized teams.

Product Highlights

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  Full Training Pipeline

  Supports the full training lifecycle—from (incremental) pre-training and multimodal instruction tuning to RLHF (Reward Modeling, PPO/DPO/KTO, etc.)—designed to meet diverse business and industry needs.

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  Efficient Fine-Tuning

  With parameter-efficient approaches like LoRA, significantly reduces parameter size and computational/storage costs, achieving the right balance between model performance and resource consumption.

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  Alignment Optimization

  Provides fine-grained RLHF configuration combined with multiple optimization algorithms, ensuring outputs are aligned with human values and specific business requirements.

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  Low-Code Compatibility

  Zero-code AutoML workflow, fully compatible with mainstream model architectures and domestic chips, offering both technical depth and ease of use.

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  Automated Dataset Distillation

  Enables fully automated dataset distillation by constructing domain taxonomies, generating domain-specific questions, and leveraging large models to create high-quality answers and reasoning processes—helping enterprises efficiently prepare training data.

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  Unified Data Management

  Delivers advanced dataset generation features (e.g., document-based Q&A), with centralized dataset management that allows users to view, edit, and maintain all generated content.

Key Features

Visualized Fine-Tuning

Provides an intuitive interface for configuring fine-tuning parameters, supporting the full workflow of training, evaluation, inference, and model export.

Model Evaluation

Enables evaluation of trained models with validation datasets, with configurable parameters such as truncation length, max samples, and batch size.

Inference Debugging

Supports inference testing with multiple configurable parameters, including max generation length, Top-p sampling, temperature, skip special tokens, escape HTML tags, and enabling reasoning.

Diverse Training Algorithms

Supports a wide range of algorithms, including (incremental) pre-training, (multimodal) instruction fine-tuning, reward modeling, PPO, DPO, and KTO training.

Application Scenarios