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Tinker SDK Backend

The Tinker backend uses Tinker’s hosted inference and training APIs. No local GPU is required. Training runs against Qwen/Qwen3-30B-A3B via API credits.

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Requirements

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Installation

1

Clone and install

2

Configure environment

Edit .env.tinker and set TELEGRAM_BOT_TOKEN and TINKER_API_KEY.
3

Start the stack

No model download needed. Inference and training happen remotely via Tinker.
4

Verify

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Services

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Configuration

These variables are set in .env.tinker. For the full Hydra config and all environment variables, see the Configuration Reference.

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Known Gotchas

Tinker model naming: Tinker uses its own model identifiers that differ from HuggingFace names. For example, the HuggingFace model Qwen/Qwen3-Coder-30B-A3B-Instruct is Qwen/Qwen3-30B-A3B in Tinker. Sampling works with either name, but LoRA training init will reject the HuggingFace name with a 400 error. Always use the Tinker name.
CLAAS_TINKER_BASE_MODEL must match base_model: The proxy reads CLAAS_TINKER_BASE_MODEL to initialize its sampling client, and the eval config’s base_model is passed to the API for LoRA init. If they reference different models, scoring and training will target different models.