Qiantong Technology
Unify models, data, training, and evaluation to build enterprise-specific industry LLMs
qBrain is a one-stop training and management platform for building enterprise industry LLMs. It bundles and centrally manages mainstream open-source and private base models, and supports multiple fine-tuning and alignment methods including SFT, LoRA, and DPO. With comprehensive data governance and training monitoring plus automated and manual evaluation, it continuously improves business alignment. After training, it automatically retains full-pipeline artifacts and supports weight merging and export — making industry LLM development manageable, evaluable, reproducible, and deliverable end to end.
Connecting models, data, training, evaluation, and delivery into a closed industry-LLM loop
Hard model selection, high training barriers, hard evaluation, hard artifact management
Base models and training data are heterogeneous and lack unified management, demanding extensive format conversion and cleansing that slows project startup.
Complex fine-tuning parameters and varied environments make selection and estimation hard without experience; troubleshooting relies on low-level logs, so diagnosis and recovery are slow.
Without unified evaluation, results are hard to compare; scattered artifacts and disconnected versions make regression hard to attribute and delivery impossible to fully reconstruct.
From industry data to proprietary models — the complete LLM development workflow
Flexible training, transparent process, comprehensive evaluation, traceable delivery
The platform bundles mainstream base models such as Qwen, DeepSeek, Llama, and ChatGLM, uniformly showing parameter size, context length, license, precision format, and trainable status.
It can ingest local, Hugging Face, object-storage, and external models, and link them to fine-tuned versions, evaluation reports, training data, and deployment status.
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Support SFT, LoRA, QLoRA, full-parameter fine-tuning, and DPO preference alignment to meet training needs across business goals, data scales, and compute conditions.
Each method gets tailored parameter forms, compatibility checks, and resource estimates that lower the expertise barrier for industry model training.
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Provide governance capabilities including field mapping, format validation, deduplication, null handling, length filtering, anomaly detection, and encoding repair.
Build a complete quality profile and link each run to a fixed dataset version, cleansing rules, field mapping, and splitting strategy to ensure reproducibility.
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Show training and validation metrics, Loss curves, learning rate, GPU utilization, memory usage, training throughput, and checkpoint status in real time.
Support pause, stop, and exception retry, with readable causes and remediation for common issues to improve training management and troubleshooting.
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Link base model, training dataset, training method, parameter configuration, training logs, evaluation results, and model artifacts to a model version.
Support automated metrics, historical-version comparison, and manual A/B evaluation; after training, save LoRA adapters, merge weights, and export delivery packages with audit records.
Contact UsCovering industry-LLM development across manufacturing, water, public sector, AI, and energy
