Core Service
Deep Learning Systems
Fine-tuning with PEFT/LoRA, RLHF/RLAIF, evaluation suites, safety
Overview
Deep learning systems power the most sophisticated AI applications—from vision and speech to multimodal understanding. Our expertise spans parameter-efficient fine-tuning, reinforcement learning from human feedback, and safety-critical evaluation frameworks that ensure your models perform reliably and align with human values and business requirements.
Key Features
PEFT/LoRA Fine-Tuning
Parameter-Efficient Fine-Tuning and Low-Rank Adaptation to customize billion-parameter models with minimal compute and memory—ideal for domain adaptation.
RLHF & RLAIF
Reinforcement Learning from Human Feedback and AI Feedback to align models with preferences, reduce hallucinations, and improve instruction-following.
Vision & Speech Models
State-of-the-art models including SAM (segmentation), CLIP (vision-language), DETR (detection), Whisper (speech), and MMS (multilingual speech).
Multimodal Systems
Integrate vision and language with LLaVA, BLIP, and custom architectures for visual question answering, image captioning, and document understanding.
Technical Approach
Our deep learning methodology ensures robust, aligned models:
- Model Selection: Choose foundation models (Llama, Mistral, SAM, Whisper) based on task requirements and deployment constraints
- Data Curation: Assemble high-quality training data with human annotation, synthetic generation, and active learning
- Training Strategy: Apply LoRA, QLoRA, or full fine-tuning with distributed training across multi-GPU clusters
- Alignment: Implement RLHF pipelines with reward modeling, PPO optimization, and safety red-teaming
- Evaluation: Comprehensive testing on benchmark datasets, custom eval sets, and adversarial examples
Use Cases
Deep learning systems enable advanced AI capabilities:
- Domain-Specific LLMs: Fine-tune language models for medical, legal, or technical domains with specialized knowledge
- Visual Inspection: Deploy SAM and CLIP for zero-shot object detection and anomaly localization in manufacturing
- Multilingual Speech: Transcribe and translate audio across 100+ languages with Whisper and MMS models
- Document AI: Extract information from forms, receipts, and contracts using multimodal models
Expected Outcomes
Deep learning systems deliver cutting-edge AI performance:
- State-of-the-art accuracy on domain-specific benchmarks
- 90% reduction in fine-tuning cost with PEFT vs. full fine-tuning
- Aligned models that follow instructions and respect safety constraints
- Production-ready systems with comprehensive evaluation and monitoring
Ready to Build Advanced AI?
Let's discuss how deep learning can power your next-generation AI applications.