LoRA Fine-Tuning Expertise

Efficient model adaptation using parameter-efficient methods:

  • PEFT Frameworks: Hugging Face PEFT with LoRA, QLoRA, and AdaLoRA
  • Configuration Tuning: Alpha scaling, dropout setting, rank control, and module targeting
  • Training Optimization: Mixed precision, gradient checkpointing, and multi-GPU DDP
  • Adapter Composition: Multi-task systems using adapter merging and task routing
  • Quantized Inference: Efficient model deployment with QLoRA and quantization support
  • Monitoring Tools: LoRA inference performance metrics, latency analysis, model versioning

Implementation Examples

  • QLoRA Integration: Fine-tune LLMs on commodity GPUs using quantized training flows
  • Multi-Task Model: Compose multiple LoRA adapters for cross-domain applications
  • Inference Optimization: Use quantized adapters for fast, low-memory API serving
  • Adapter Merging: Combine domain-specific adapters into a single production model
  • Custom DSL Adapter: LoRA fine-tuning for internal documentation and structured data

Fine-Tuning Process

  1. Model Analysis Identify target modules, available compute, and desired adaptation outcomes

  2. Training Pipeline Build training loop, apply LoRA configuration, and run optimized training

  3. Deployment & Optimization Merge adapters, quantize, and deploy to production with latency monitoring

Investment & Pricing

  • Basic LoRA Training: $15K–30K Single adapter training for a narrow task or domain

  • Advanced LoRA Platform: $30K–60K Multi-adapter or QLoRA system with optimized inference

  • Enterprise Deployment: $60K–120K+ Production platform with adapter management, monitoring, and scaling

  • R&D / Custom Work: $150–250/hr LoRA research, custom adapter strategies, or novel model compression pipelines

  • Ongoing Support: Monthly retainer for tuning, evaluation, and adapter lifecycle management

See LoRA in Action

Explore live demos of LoRA-powered assistants, multi-adapter models, and high-efficiency inference on constrained hardware.

Ready to Adapt with LoRA?

Let’s discuss your language model goals and fine-tuning needs. I help Triangle area teams build smarter, more efficient AI through LoRA-based development.