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Choose where your health data may be processed, then consider your hardware and image-understanding needs. Mirobody supports fully local, cloud and hybrid configurations. Chat, document extraction and journal entries can use different models.

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Deployment options

The setup page supports local or cloud models; hybrid mode requires .env settings. CPU-only answers can take several minutes, depending on the hardware, model and conversation.

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Local models

The default local configuration uses MiniCPM5-2B with Q8_0 weights. The larger configuration uses Qwen3.8-27B with IQ3_S weights and its vision projector. Both use GLM-OCR-0.9B for document recognition. Inference runs in llama.cpp’s llama-server, with models downloaded from Hugging Face on first use. See local setup for platform-specific GPU and CPU configurations.

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Documents and photos

GLM-OCR extracts text and tables from reports. Rules read recognized table layouts, and a model processes the remaining text. The lightweight chat model receives a photo’s OCR text and cannot infer its visual appearance. The larger chat model can inspect the image directly, but its interpretation, including a meal’s identity or calorie estimate, may still be wrong.

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Data sent to cloud models

Cloud models reduce local hardware requirements. After configuring a key, choose an available entry in the model picker. Local OCR keeps document images local; text can still leave. Chat images follow the chat model’s settings separately from uploaded files.

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OpenRouter routing

OpenRouter may route requests among hosting providers, whose retention terms and supported parameters differ. A setup-page key or OPENROUTER_CHAT_MODEL does not pin a host. Review both account data settings and the host’s terms. The recorded evaluation required zero data retention, with one host per model and no fallback. These settings use provider.order and allow_fallbacks: false in extra_body; see the reference configurations. They control routing and retention, while processing remains external.

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Hybrid configuration

Start the local model server with GLM-OCR available, then set:
LOCAL_OCR_BASE_URL sends report images to local OCR even with a cloud key present. UTILS_VISION_MODEL=local-utils keeps files without extractable text, such as meal photos, local too: they receive a local description or none. For chat images, set supports_image: false on the cloud chat entry so it receives OCR text. Configure hybrid mode in .env and the model entries.

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Switching and checking models

  • Setup page: open the link printed by ./deploy.sh, or go to Settings → Model. Enter a key and model name, or choose 100% on this machine and a local configuration. The choice is checked, encrypted and applied without a restart.
  • Environment settings: LOCAL_MODEL and LOCAL_OCR_MODEL select local models. OPENROUTER_CHAT_MODEL and OPENROUTER_UTILS_MODEL select the models behind an OpenRouter key. DEFAULT_MODEL, UTILS_TEXT_MODEL, UTILS_VISION_MODEL and UTILS_OCR_MODEL select configuration entries. After changing .env, run docker compose up -d; a restart alone does not reload it. See Configuration and config.llm.yaml for advanced settings.
Check the selected routes with:
The probe checks a streamed chat tool round trip, plus document extraction, vision and OCR, and reports whether the chat model showed its thinking. A model that thinks adaptively may skip it on the probe’s short question, so no thinking there is a note, not a failure. It also verifies local model names. A successful probe confirms those capabilities, not answer quality or expected latency.

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Thinking and the final answer

Chat requests thinking through supported interfaces. AGENT_THINKING defaults to medium; an enabled entry-level thinking_level can override it. Utility tasks retain their settings. The work panel shows returned thinking and tool activity; final text appears after its model step finishes. Some endpoints withhold reasoning, which the probe reports; Mirobody does not invent it.

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Evaluation reference

See the evaluation guide, historical results and OCR benchmark for methods, measured results and reproduction commands. Results use synthetic records and apply to the recorded versions, hardware and providers.