Predibase

Managed fine-tuning API with low-level control over the training loop.

Tinker is Thinking Machines Lab's training API for fine-tuning open-weight LLMs. It exposes low-level primitives — forward_backward, optim_step, sample — so researchers keep full control of data and algorithms while the service handles distributed GPU scheduling and failure recovery. LoRA-based runs cover models from small Llamas up to large mixture-of-experts like Qwen-235B and Kimi K2, and trained weights can be downloaded.

Replace frontier-model spend with a fine-tuned small model.

Captures your production OpenAI / Anthropic calls, builds a dataset, fine-tunes a small open-weights model on your traffic, then serves the swap behind your existing SDK. The pitch: 10x cost reduction at parity.

Fine-tune open LLMs 2x faster with far less VRAM. Open source.

An open-source (Apache-2.0) framework for fine-tuning and running open-weight models with custom CUDA kernels — roughly 2x faster training and large VRAM savings, so 7B–13B models fit on a single consumer GPU. Free tier runs on Colab/Kaggle or locally; Pro and Enterprise tiers add multi-GPU and multi-node speedups. Exports to GGUF/Safetensors for llama.cpp, vLLM, and Ollama.