Cognitio

Overview #

Cognitio writes a short, friendly reply to small talk. You give it the latest message and a few earlier turns; it gives back one or two casual sentences. Use it when a conversation is a check-in or a quick question rather than a job, and a large language model would be slower and dearer than the moment deserves.

Cognitio is a conversational responder: given the current turn and a short window of prior turns, it returns one or two short sentences in a casual, warm register. It is the Response-family model a host application calls when a conversation is small talk rather than a job — a check-in, a quick opinion, a one-line question — and a full-size responder would be slower and more expensive than the moment deserves.

Cognitio is a fine-tune, not a model trained from scratch. It applies an 18.5M-parameter LoRA adapter, trained by Ducky Software, to Qwen2.5-1.5B-Instruct, an open-weight 1.5B-parameter instruct model. The adapter shapes the base model's voice and length; the base model supplies the language ability. Input is the current user turn plus up to eight prior turns, rendered by the service as a ChatML conversation behind a fixed system prompt; output is a single reply string capped at 96 new tokens.

Within the response family, Cognitio is the only member since ERM, the fixed-line reserve responder, was withdrawn on 2026-09-20: it generates free text on a GPU. Cognitio has no classification heads and no label catalogue; it does not decide whether a reply is safe to give. A host that needs that check runs an intention model such as ELIM before calling Cognitio.

Intended use #

• Short conversational replies — greetings, check-ins, casual opinions, quick how-tos — where one or two sentences are the right length.
• Keeping a consistent, brief voice across a conversation without prompting a large model for every turn.
• Serving as the ordinary responder in a host that already runs an intention check before it.

Out of scope #

• Long, technical, or multi-step answers; the 1.5B base and the 96-token cap both work against them.
• Anything that needs current facts, retrieval, or tools; Cognitio has none and will guess.
• Safety screening or content moderation of its own input or output.

Choose Cognitio when #

• The host needs a generated reply in a consistent casual register and can afford a GPU-backed HTTP call of a few seconds.
• The conversation window is short (at most nine turns) and the expected reply is one or two sentences.
• A host that needs a deterministic fallback while the service is unavailable holds a fixed line of its own; no Falcon model plays that part.

Specification #

Try it #

You send

A chat that opens with “hey” and then asks “you free later?”.

You get back

A short, casual reply, such as asking what time the person was thinking of.

The same exchange as the API sees it:

{
  "prompt": "you free later?",
  "history": [
    { "role": "user", "content": "hey" },
    { "role": "assistant", "content": "hey — what's up?" }
  ],
  "max_new_tokens": 64,
  "temperature": 0.7
}

{
  "text": "might be — what time were you thinking?",
  "model": "cognitio",
  "base_model": "Qwen/Qwen2.5-1.5B-Instruct",
  "backend": "friend-lora"
}

Limits & safety #

Cognitio does not see the host application's tools, memory, user profile or any earlier part of the conversation: it reads at most nine turns rendered as ChatML text behind a fixed system prompt, and nothing else. It does not see images, locations, dates or the real time.

• It has no retrieval, no tools and no current data; questions about weather, prices, schedules or news receive a plausible guess, not an answer.
• It is small: long, technical or multi-step requests exceed what a 1.5B base and a 96-token cap can do well.
• It performs no input or output moderation of its own; the training filter removed assistant-register and inappropriate phrasing from the data, but nothing at inference checks the request or the reply.
• It is non-deterministic at the default temperature; the same request can produce different replies, and an empty or truncated reply is possible.
• Its voice is fixed by the training persona (lower-case, em dashes, Canadian references) and cannot be steered per request; there is no persona or style parameter.
• Its context is short: history beyond the last eight turns is silently dropped, and multi-turn coherence was not evaluated.
• The service has no authentication and no rate limiting; access control belongs to the deployment.

Related models #

Latest versions #