MULTIMODAL MODEL ENGINEERING
An integrated engine for parameter-efficient tuning, task capability distillation, model evaluation and private training, supporting industry model services across public cloud, dedicated cloud and local environments.
CORE CAPABILITIES
Three connected model-engineering capabilities for lightweight multimodal models in vertical industries.
LoRA, QLoRA and P-Tuning v2 support multimodal image, text and video tasks while adapting industry knowledge and task capability with minimal impact on general foundation-model capability.
LoRA, QLoRA and P-Tuning v2
Adapters, prompt parameters, cross-modal projection layers and selected model parameters
Single GPU, multi-GPU on one machine and distributed multi-node training
Transfer effective capability from high-capacity teachers into smaller multimodal students for industry recognition, understanding, Q&A and analysis tasks.
Output-distribution distillation, cross-modal representation alignment, intermediate-feature distillation and task supervision
Industry recognition, understanding, Q&A and analysis
Constrained by industry task evaluation to reduce inference cost while retaining task usability and stability
Package data processing, parameter-efficient tuning, knowledge distillation, model evaluation and version management as a complete independently deployable training service in the customer environment.
Data processing, parameter-efficient tuning, knowledge distillation, model evaluation and version management
Model training, evaluation, version release and continuous iteration
Training data, model weights, evaluation results and training artifacts remain within the customer-authorized boundary
ENGINEERING FOUNDATION
Detection and classification models, CLIP-based ranking models, and a dual VLM + LLM system.
Experience cleaning and governing more than ten million samples, supported by data scientists.
A delivered case in which 20 scenarios in one security domain were tuned successfully in one batch.
End-to-end synthetic data enhancement across compact, mid-size and VLM models.
START WITH A REAL TASK
Share the task, available data, evaluation criteria and deployment boundary. Our algorithm and engineering teams will shape the first validation path.