Everything you need to fine-tune, deploy, and manage custom LLMs
Fine-Tune Lab is a complete platform for training custom AI models. From dataset upload to production deployment, we handle the complexity so you can focus on results.
Train custom AI models on your data without PhD-level ML knowledge. Upload dataset, click train, get results.
Upload, validate, and manage training datasets with built-in quality checks and format verification.
Monitor loss curves, learning rates, and GPU utilization in real-time as your model trains.
Deploy trained models to production with one click. Cloud deployment via RunPod Serverless with automatic scaling and production-ready API endpoints.
RESTful API with 25+ endpoints. Complete SDK support for Python, JavaScript, and cURL.
Multi-tenant architecture with row-level security. Your data stays yours, always encrypted.
Track every training run, compare results, and roll back to previous checkpoints instantly.
Pause, resume, or cancel training jobs. Adjust hyperparameters on the fly without starting over.
Compare training runs, identify best hyperparameters, and optimize for cost or performance.
Automatic batch size tuning, gradient accumulation, and mixed precision for maximum GPU efficiency.
Train models on your support tickets to answer customer questions in your brand voice.
Fine-tune Llama on 5000 historical support conversations → 80% ticket deflection rate
Create AI coding assistants that understand your codebase conventions and patterns.
Train on your GitHub repos → Generate boilerplate in your team's style
Build specialized models for legal, medical, or financial applications with domain knowledge.
Fine-tune on medical papers → Answer clinical questions with citations
Generate marketing copy, product descriptions, or social media posts in your brand tone.
Train on past campaigns → Generate on-brand content at scale
Upload a JSONL file with your training examples. We validate format and provide quality metrics automatically.
Choose your base model (Llama, Mistral, etc.) and set hyperparameters. Or use our recommended defaults.
Click start and watch real-time metrics as your model learns. Pause/resume anytime.
Deploy to RunPod Serverless with auto-scaling cloud inference. Set budget limits and get a production-ready API endpoint within minutes.
Track usage analytics, compare model versions, and iterate with new training data.
Hugging Face
Model Training
RunPod
Cloud GPU & Inference
RunPod Serverless
Cloud Inference
PyTorch
Deep Learning
Supabase
Database & Auth
Next.js
Frontend
CUDA
GPU Acceleration
Docker
Containerization
Redis
Job Queue
Follow our quick start guide to train your first custom model in under 10 minutes.