Fine-tuning
Train and host custom models on your own data.
33 tools
Fine-tuning has gone from "deep ML team only" to "a few hours of JSONL away" — but the choice between closed-model FT (OpenAI), open-model FT (Together, Modal), and memory-tuning matters more than ever.
Covers closed-model fine-tuning (OpenAI), open-model FT + serving (Together AI, Replicate, Modal), distributed training platforms (Anyscale), and specialised platforms (Lamini for factual recall).
Pick OpenAI for the easiest UX on closed models. Pick Together AI for open-model FT + serving in one place. Pick Modal for serverless GPU control. Pick Lamini specifically for hallucination-free factual recall.
Paperspace Gradient
End-to-end MLOps platform with GPU notebooks, training jobs, and model deployment, now folded into DigitalOcean.
H2O AutoML
Open-source automated machine learning that handles feature engineering, model selection, and stacked ensembling out of the box.
Scale GenAI Platform
Enterprise agent platform from Scale AI that connects your data, orchestrates multi-agent workflows, and learns from human feedback inside your own VPC.
W&B Sweeps
Hyperparameter optimization from Weights & Biases with Bayesian search and Hyperband early stopping.
Forefront
Fine-tune and serve open-source LLMs on your own data without managing GPUs.
ONNX
Open standard for representing and exchanging machine learning models across frameworks and runtimes.
Apache SINGA
Apache-licensed distributed deep learning library focused on scalable training across GPUs and nodes.
DagsHub
GitHub-style collaboration platform for ML datasets, experiments, and models with MLflow and DVC under the hood.
Velda
Serverless GPU orchestration that runs AI training and batch jobs without Docker or Kubernetes.