Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
Stage
MVP
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Founded
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Based in
FR
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Pricing model
Paid
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Pricing
Pay-as-you-go credits. Top-up fees: Business rate 5.5% + €0.30, Standard rate 6.5% + €0.30, International 8.5% + €0.30. Minimum top-up €5.00. Tokens billed at upstream provider's per-token price in EUR with no per-token markup.
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Key features
OpenAI-compatible API, works with existing SDKs and tools without modification
Per-request carbon emissions tracking and grid carbon intensity reporting
Multi-provider routing with choice of provider, model, and region per request
Model aliases for repointing requests without code changes
Auto-routing to select lowest-carbon route based on policy
Jurisdiction filtering including EU-only provider option
Usage dashboard with per-model, per-provider, and per-key breakdown
CSV export for auditable per-request usage and carbon data
One invoice for all providers with automatic EUR conversion
No lock-in with base URL configuration allowing instant provider switching
forward_backward() operation for computing loss and accumulating gradients
optim_step() for updating the adapter
sample() for generating from current or saved policy
save_state() to save and resume training state
Tinker API compatibility
Separate trainer and sampler resources
Synchronous and asynchronous RL patterns
Frozen base model weights with LoRA adapter updates
Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
What makes it different
Sovereign EU hosting outside CLOUD Act reach with GDPR compliance and your choice of provider jurisdiction per request
No data training or retention; prompts forwarded unchanged to chosen provider only
Managed training and sampling infrastructure, no setup required
Tinker-compatible API for easy migration
Ready-made SFT and RL recipes through Loops cookbook
Loops