From Foundation Model to Domain Expert
Fine-tune open-source foundation models on your proprietary data to build domain-specific AI that understands your business, your terminology, and your edge cases. Our training team handles the entire workflow: data preparation, training infrastructure, hyperparameter optimization, evaluation, and deployment, so you get a production-ready model, not a research experiment.
We clean, format, and validate your training data. Synthetic data augmentation for underrepresented classes. Quality scoring and filtering to ensure training data meets your accuracy targets.
LoRA, QLoRA, and full fine-tuning depending on model size and budget. Models over 13B parameters get QLoRA by default for cost efficiency. Smaller models can be fully fine-tuned.
Multi-GPU and multi-node training on optimized infrastructure. We handle FSDP, DeepSpeed, and Megatron-LM configuration so you don't have to.
Automated hyperparameter search across learning rates, batch sizes, warmup schedules, and LoRA configurations. We find the sweet spot between training cost and model quality.
Comprehensive evaluation against your custom benchmarks, public leaderboards, and domain-specific test sets. We report accuracy, latency, and quality metrics before and after fine-tuning.
Fine-tuned models are delivered with inference-optimized weights, quantization recommendations, and deployment configurations ready for the inwire platform or your own infrastructure.
Share your goals and constraints. We'll map a practical path to production.
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