Best AI tools for open model fine tuning

Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).

Serverless GPUs and infra for training & serving ML.

One-API platform for running and fine-tuning open-source models.

Meta's open-weight LLM family covering 1B mobile models up to 405B frontier and natively multimodal 10M-context Llama 4 variants.

Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.

Declarative, YAML-driven deep learning framework for fine-tuning LLMs and multi-modal models without writing training loops.

Fine-tuning and reinforcement learning platform for turning expensive prompts into cheap, fast, task-specific models.

Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM.

No-code fine-tuning and training pipeline that spins up state-of-the-art models on the Hugging Face Hub.

On-demand NVIDIA GPU cloud built specifically for training, fine-tuning, and serving large AI models.

Open-source Python framework for automated hyperparameter optimization across any ML stack.

Open-source Python library for distributed hyperparameter tuning at any scale.

Managed fine-tuning platform for open-source LLMs and vision models with LoRA, full fine-tuning, and RL support.

End-to-end platform for training and deploying ML models on microcontrollers, sensors, and other edge hardware.

Ray-powered platform for training, serving, and scaling LLMs.

Production inference and fine-tuning platform for open-source LLMs, tuned for speed and enterprise economics.

Memory-tuning platform for grounding LLMs in your facts.

Distributed training, fine-tuning, and serving platform with federated learning roots.

Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.

End-to-end MLOps platform with GPU notebooks, training jobs, and model deployment, now folded into DigitalOcean.

Open-source automated machine learning that handles feature engineering, model selection, and stacked ensembling out of the box.

Hyperparameter optimization from Weights & Biases with Bayesian search and Hyperband early stopping.

Fine-tune and serve open-source LLMs on your own data without managing GPUs.

Open standard for representing and exchanging machine learning models across frameworks and runtimes.

Apache-licensed distributed deep learning library focused on scalable training across GPUs and nodes.

GitHub-style collaboration platform for ML datasets, experiments, and models with MLflow and DVC under the hood.

An open-source optimization layer that sits between your app and Ollama to squeeze more performance out of local LLMs.

Making large AI models cheaper, faster, and more accessible through distributed training

Learn how LLMs work by building one on your Mac

The deep learning framework for professional AI researchers and ML engineers