# River

River AI provides a managed API from training to deployment for developers and teams who want to have their own models; the platform runs LoRA fine-tuning and reinforcement learning based on open source large models. Users can bring in their own data and reward functions to let the model learn specific tasks. The weights and checkpoints after training can be saved, iterated, and services provided through an OpenAI-compatible interface. [Core Function] LoRA low-rank fine-tuning: Update model weights through configurable adapters, train on modules such as attention, MLP, and MoE experts to quickly shape models for specific tasks. Reinforcement learning training: supports the RL process based on custom rewards, completes sampling, scoring, updating and other steps to help the model continue to improve against actual goals. Various open source bases: Provide open source models such as Qwen, Kimi, GLM, DeepSeek, Nemotron, etc., covering parameter scales from approximately 35B to 1T. The specific available models depend on account permissions. Python client and deployment: Sampling, training and saving checkpoints are unified through river-client. The training results can be directly connected to existing applications using OpenAI compatible endpoints.

River AI provides a managed API from training to deployment for developers and teams who want to have their own models; the platform runs LoRA fine-tuning and reinforcement learning based on open source large models. Users can bring in their own data and reward functions to let the model learn specific tasks. The weights and checkpoints after training can be saved, iterated, and services provided through an OpenAI-compatible interface. [Core Function] LoRA low-rank fine-tuning: Update model weights through configurable adapters, train on modules such as attention, MLP, and MoE experts to quickly shape models for specific tasks. Reinforcement learning training: supports the RL process based on custom rewards, completes sampling, scoring, updating and other steps to help the model continue to improve against actual goals. Various open source bases: Provide open source models such as Qwen, Kimi, GLM, DeepSeek, Nemotron, etc., covering parameter scales from approximately 35B to 1T. The specific available models depend on account permissions. Python client and deployment: Sampling, training and saving checkpoints are unified through river-client. The training results can be directly connected to existing applications using OpenAI compatible endpoints.

Canonical: https://uniquebase.ai/products/river
Stable identifier: UB-D6899
Snapshot captured: 2026-10-28T00:47:51.550Z
English updated: 2026-10-28T00:56:07.026Z

## Connected profiles

- [River](/companies/river)

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