tinyllama:1.1b

Measured by one node on hardware we do not own, and signed by each of them.

Context declared

2,048

what the runtime reported. A node attests it was told this; nobody attests that it is true.

Context proved by recall

not probed

the largest size at which a machine could still find a token planted at the far end.

What the runtime declared

Quantisation

Q4_0

Parameters

Family

llama

Format

gguf

ollama /api/show; the node attests that the runtime reported these, not that they are true. Attested by did:epn:002408…9e3c79.

Tool calling — probed, and failed
The node asked the model to call a function and checked whether it produced a valid call. Passing here is not the same as reasoning correctly over what the tools returned — a smaller model can do the first and fail the second silently.
Tool loop finished — probed, and failed
Whether the conversation ENDED after the tool call, or the model kept calling until the turn budget ran out. Passing “tool calling” and failing this means the model will call a tool in production and never come back. A model chosen on the first flag alone is the reason an agent silently stops delivering work.
Vision — probed, and failed
Shown an image and asked what was in it. The node checked the answer.
Audio — probed, and failed
Given audio and asked to transcribe it.
Extended thinking — probed, and failed
The model emits reasoning before its answer. Measured where the probe could observe it.
Structured output — probed, and failed
Asked for JSON matching a schema, and got it. This is the difference between a model you can build on and one you must parse defensively.

Nothing below is averaged. A model is fast on one machine and slow on another, and the machine is what you are choosing.

Tell us the workload, whose machines it should run on, and which jurisdiction it answers to.