Developer reference

The models palOMine ships with.

Nine open-weight models, pre-tuned end-to-end on a single GMKtec EVO X-2. No API keys, no cloud — every weight below runs on the appliance, in your network, behind your gate.

What this page is

A reference doc — not a marketplace.

These are the models the appliance ships with. They are not user-swappable; palOMine is pre-tuned end-to-end on this stack, and changing a single model invalidates the tuning the rest depend on. This page documents the actual stack for developers evaluating the device — what each model is, what the appliance uses it for, and what it is good at.

  • Main reasoning & coding agent
    NVIDIA Nemotron-3.5-Lightning (30B, quantized GGUF)

    The primary agent that reads, edits, and runs code. Plans multi-step work, drives the coding assistant, and answers long-form reasoning prompts.

    Capability: Large-context code + reasoning; the heaviest model on the box.

  • Background / secondary model
    Qwen3.5-2B

    Handles lightweight side-tasks — short classifications, summaries, watchdogs — so the main agent stays focused on the user turn.

    Capability: Small, cheap, and fast — the appliance default for anything that is not the main agent.

  • Speech-to-text
    Whisper-Large-v3-Turbo

    Transcribes dictated prompts and chat messages from the chat gateways and the web UI. First stage of every voice-in surface.

    Capability: Multilingual ASR with a good latency/quality trade-off.

  • Text-to-speech
    Kokoro

    Speaks agent responses back through the chat gateways and the on-device web UI. The voice half of every voice-out surface.

    Capability: Low-latency TTS, sized for natural conversational pacing.

  • Streaming speech recognition
    Moonshine-Medium-Streaming

    Low-latency live captioning-style input — listens while the user speaks instead of waiting for a finished utterance, for dictation flows that need to feel reactive.

    Capability: Streaming STT — incremental, turn-taking-friendly transcription.

  • Image generation
    FLUX-2-Klein

    Generates images from prompts inside the appliance — illustrations, diagrams, generated artwork — entirely on-device.

    Capability: Small, fast image generation tuned for the appliance memory budget.

  • Image upscaling
    RealESRGAN-x4plus

    Upscales generated or imported images 4× for higher-resolution outputs without re-running the diffusion model end-to-end.

    Capability: Super-resolution — sharpens low-resolution sources before they leave the box.

  • Embeddings
    nomic-embed-text-v1

    The vector backbone for retrieval and long-term memory: every note, file, and conversation gets projected into a vector store on the appliance.

    Capability: Text embeddings, sized for high-quality on-device retrieval.

  • Reranking
    bge-reranker-v2-m3

    Reranks candidate passages returned by the embedding-based retriever so the top results the agent sees are actually relevant, not just nearest-neighbour.

    Capability: Cross-encoder reranker — the precision step after vector recall.

See the appliance in the product spec.

The model list above mirrors the stack documented on the product page, alongside the hardware it runs on and the real-world performance numbers measured on the appliance.

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