LLM Fine-Tuning Services
Transform foundation models into domain experts. We offer the full spectrum of fine-tuning techniques, from parameter-efficient LoRA to full RLHF alignment.
Fine-Tuning Techniques
LoRA
Low-Rank Adaptation for parameter-efficient training. Fast iteration with minimal GPU footprint.
QLoRA
Quantized LoRA for fine-tuning 70B+ models on a single node with 4-bit precision.
Full Fine-Tuning
End-to-end weight updates when maximum domain adaptation is required.
RLHF
Reinforcement Learning from Human Feedback to align model outputs with human preferences.
DPO
Direct Preference Optimization. A simpler, reward-model-free alignment approach.
Dataset Management
Data curation, deduplication, quality filtering, and synthetic data augmentation pipelines.
End-to-End Pipeline
Upload Data
Bring your datasets in any format. We handle cleaning and formatting.
Configure
Choose base model, technique, hyperparameters, and evaluation criteria.
Train
Distributed training on our GPU cluster with live loss dashboards.
Evaluate
Automated evals, human preference testing, and safety benchmarks.
Deploy
One-click deployment to our inference cloud or export weights to yours.
Ready to Fine-Tune?
Share your use case and dataset. We will recommend the best technique and deliver a production-ready model.
Start a Project