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A full context breaks three local models, each in a different way - 7

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In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server hands a question to a local Ollama model. It asks for the answer as Adaptive Card JSON, a strict, closed-vocabulary schema that a Flutter client renders as interactive UI rather than as text. A set of probes in that repository puts identical questions to different local models. Probe context_fill_probe.dart  produced every figure in this article. It runs 25 test cases, one question each. A test case passes only if the reply used an element type that would answer the question. Every other probe in that set had been asking into a nearly empty window. That window holds a system prompt, one question, and at most a short canned exchange showing the model the shape of a card. The literature documents that a long context degrades model behavior. What it does to a model's adherence to an output format is measured less often. The test probe simulates a long conversation by filling three q...
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Ollama silently drops a history message larger than its context window - 6

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In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server hands a question to a local Ollama model. It asks for the answer as Adaptive Card JSON, a strict, closed-vocabulary schema that a Flutter client renders as interactive UI rather than as text. A directory of probes measures which local models manage that and how well. Every one of those probes had been asking its question into a nearly empty context window. Each call carried a system prompt, one question, and at most a short seed exchange. A real conversation fills the window. What changes when it is full? Two findings came out of filling it, measured on an Apple M1 Max with 64 GB and an Apple M5 with 16 GB. Both hosts ran Ollama 0.33.3, and the M1 Max results reproduced under Ollama 0.34.0. What the runner allocates follows one rule , with no counterexample in fifty-one runs across the two Ollama versions. It is often not what the request asked for. A history m...
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Eight measurement rules from a local-model benchmark on Ollama - 5

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In freemansoft/Flutter-AdaptiveCards  a demonstration Flutter client sends questions to a Dart chat server. The server passes each one to a local Ollama model and asks for the answer as Adaptive Card JSON. The client renders the card that comes back. To measure which models manage that, a directory of probes sends a fixed set of questions straight to Ollama, one model at a time. Each probe builds its requests the way the chat server does and judges every reply with the server's own card detector. Several of those results looked like something a model did when the cause was the test setup: the machine, the Ollama runtime, the harness or the probe. Each rule below checks for one of those mistakes before it reaches a published number. Five of the eight rules come from a measurement that went wrong. The other three guard against a known weakness in the setup: the per-call time limit, the point in a long run where a model is measured, and what the card detector cann...
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Ollama's tool channel beats prose for card JSON on every model that calls it - 4

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In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server hands a question to a local Ollama model. It asks for the answer as Adaptive Card JSON, a tree of typed UI components called elements ( TextBlock , Table , Input.ChoiceSet ). A Flutter client renders that card as interactive UI rather than as text. By default, the card comes back in the model's message body, as JSON text inside message.content , which the chat server parses to recover the card. Ollama also offers a second route, the tool channel. The request body declares a render_adaptive_card function and a schema for its arguments, in the standard tool-calling format. Nothing ever runs that function. It exists only to give the model a schema to answer into. When the model uses it, the card arrives in message.tool_calls[0].function.arguments . Ollama has already decoded it into a JSON object, so there is no text for the chat server to parse. We measure the tool channel t...
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