Fine-Tuning
Adapt open-source foundation models to your domain with LoRA, QLoRA, and full supervised fine-tuning.
Fine-tuning adapts pre-trained foundation models to specific domains and tasks. Using parameter-efficient methods like LoRA and QLoRA alongside full SFT, FORGE achieves high-accuracy domain adaptation with 10x less data through physics-informed architectures and synthetic augmentation.
Domain adaptation via parameter-efficient or full fine-tuning.
What's Included
LoRA & QLoRA Adaptation
Parameter-efficient fine-tuning of Llama 3, Qwen, and other open-source models with minimal compute overhead.
Long-Context Training
Support for context windows up to 100k tokens for complex document understanding and reasoning.
Structured Outputs
Train models to produce reliable JSON, SQL, code, and domain-specific structured formats.
Domain Terminology Alignment
Adapt model vocabulary and behavior to domain-specific terminology and conventions.
Sparse-Data Optimization
Physics-informed architectures and synthetic augmentation for high performance with limited training data.
Specs & Parameters
Use Cases
Defense Intelligence
Adapt models to classified intelligence domains with secure, air-gapped training infrastructure.
Financial Risk
Train on proprietary financial data for risk modeling, compliance, and decision support.
Healthcare Operations
Domain-adapt for clinical terminology, medical records processing, and operational workflows.
Ready for Fine-Tuning?
Typical engagement: 3-6 weeks. From scoping to deployment, FORGE handles the full pipeline.