Precision Fine-Tuning for Your Domain

Adapt foundation models to your proprietary data with enterprise-grade efficiency. Deploy customized LLMs, vision, and multimodal models in days, not months.

Explore Pipeline → 📖 API Reference

Why Fine-Tune?

Custom Intelligence, Zero Compromise

Generic models miss context. Our fine-tuning infrastructure optimizes performance, reduces latency, and locks in domain expertise.

🎯

Domain Adaptation

Train on your private datasets to capture industry terminology, workflows, and decision logic that base models miss.

🔒

Data Sovereignty

Your data never leaves your VPC or our isolated compute clusters. SOC 2 Type II & HIPAA-ready training environments.

⚡

Cost & Latency Optimization

Fine-tuned models require 60-80% fewer inference tokens and respond 3x faster on specialized tasks compared to zero-shot prompting.

Training Techniques

Advanced Fine-Tuning Methods

Select the optimization strategy that matches your compute budget and performance requirements.

LoRA Recommended

Low-Rank Adaptation injects trainable rank decomposition matrices. Achieves 90%+ of full fine-tuning quality with only 1-10% trainable parameters.

QLoRA

4-bit quantized LoRA. Enables training 70B+ models on single A100/H100 GPUs. Perfect for budget-constrained or rapid prototyping workflows.

Full Parameter

Update every weight in the model. Highest possible fidelity for highly specialized tasks, legacy architectures, or maximum accuracy requirements.

DPO / RLHF

Direct Preference Optimization & Reinforcement Learning. Align model outputs with human feedback, reducing hallucinations and improving safety.

Workflow

From Raw Data to Deployed Model

Our automated pipeline handles preprocessing, distributed training, evaluation, and deployment with zero manual overhead.

Automated dataset cleaning & augmentation
Dynamic learning rate scheduling
Real-time loss monitoring & checkpointing
One-click deployment to NexusAI Edge

Data Ingestion & Formatting

Upload CSV, JSON, Parquet, or vector DB dumps. We auto-convert to instruction-tuning pairs or supervised formats.

Preprocessing & Validation

Tokenization, deduplication, bias filtering, and train/val/test splitting with stratified sampling.

Distributed Training

FSDP/DDP across multi-GPU clusters. Auto-scaling based on dataset size and chosen adapter method.

Evaluation & Benchmarking

Automated runs on MMLU, HELM, and custom business KPIs. Generate accuracy, latency, and drift reports.

Optimization & Deployment

GGUF/ONNX export, KV-cache optimization, and instant deployment to your dedicated inference endpoint.

Infrastructure

Supported Models & Frameworks

Native compatibility with industry-standard architectures and training libraries.

Category	Supported Architectures	Framework	Status
Large Language Models	Llama 3, Mistral, Qwen, Gemma, Falcon	PyTorch / DeepSpeed	Production
Vision & Multimodal	CLIP, SAM, BLIP-2, LLaVA	Transformers / PEFT	Production
Time-Series & Tabular	Temporal Fusion, TabTransformer	TensorFlow / PyTorch	Beta
Audio & Speech	Whisper, BART-large, Wav2Vec2	Transformers	Production

FAQ

Common Questions

For most LLMs (7B-70B parameters) using LoRA/QLoRA, training completes in 2-8 hours depending on dataset size and compute allocation. Full parameter training may take 24-48 hours on multi-node clusters. You receive real-time progress updates and can pause/resume jobs anytime.

Absolutely. We support isolated VPC deployment, on-premise training nodes, and zero-data-retention policies. Your datasets are encrypted at rest (AES-256) and in transit (TLS 1.3), and we never use customer data to improve base models.

We run automated benchmarks including perplexity, ROUGE, BLEU, and domain-specific accuracy tests. You'll receive a detailed report with confusion matrices, hallucination rates, latency benchmarks, and recommended inference settings.

Yes. NexusAI supports incremental learning and periodic retraining pipelines. Connect your data warehouse or message queue, and our platform will automatically trigger fine-tuning jobs when new data thresholds are met or concept drift is detected.