Custom Model Fine-Tuning
Fine-tune GPT, Claude, Llama, or Mistral on your dataset with hyperparameter sweeps and full training transparency.
We run your fine-tuning job on the platform that fits your base model: OpenAI for GPT-class models, Together AI for Llama and Mistral, and Hugging Face for adapter-based runs.
Every job ships with a training plan: target base model, epoch count, learning rate, batch size, and validation cadence. For larger plans we run hyperparameter sweeps and pick the checkpoint that wins on your held-out set, not on training loss alone.
You get the training logs, loss curves, and the exact configuration we used — so the fine-tune is reproducible long after delivery.
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Dataset Engineering
We turn raw CSV or JSONL exports into clean, deduplicated, schema-validated training corpora ready for fine-tuning.
Evaluation & Benchmarking
Measure your fine-tune against a held-out set with BLEU, ROUGE, faithfulness, and task-specific scoring.
API Endpoint Deployment
A ready-to-call REST endpoint with auth, rate limits, and a documented prompt schema you can paste into your stack.