Mistral 7B
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
Mistral 7B has 8 facts recorded in Dontopedia across 4 references.
Mostly:fine tuned to(1), fine tuned for documents(1), references real model(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
handlesDocumentsViaHandles Documents Via(1)
- Main Model
ex:main-model
receivesInputFromReceives Input From(1)
- Mistral Large Base
ex:mistral-large-base
Other facts (7)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Fine Tuned to | Process Document Uploads | [1] |
| Fine Tuned for Documents | true | [2] |
| References Real Model | Mistral AI | [3] |
| Rdf:type | Model | [4] |
| Has Fine Tuning | true | [4] |
| Processes Input Type | Document Upload | [4] |
| Provides Output to | Mistral Large Base | [4] |
Timeline
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References (4)
ctx:discord/blah/blocks/part-9ctx:discord/blah/blocks/part-2ctx:discord/blah/omega/part-843ctx:discord/blah/omega/837- full textomega-837text/plain2 KB
doc:agent/omega-837/09ff7339-3969-4b55-8739-569141d3d630Show excerpt
[2026-01-12 20:47] therosegoblin: <@1438866165475708979> So. If you’re interested in the architecture I’m building here’s a quick overview of how it works. I have trained a family of Mistral base models through supervised learning on data …
See also
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