Kev: Tiny Jev-like family of decision models built on top of Qwen3.5
Kev Small Jev-like decision models you can train and run yourself. Kev is a family of small decision models built on Qwen3.5 and based on the architecture described in Jev's Architecture Unmasked. You can use the pretrained weights or train your own.
- ▪Kev Small Jev-like decision models you can train and run yourself.
- ▪Kev is a family of small decision models built on Qwen3.5 and based on the architecture described in Jev's Architecture Unmasked.
- ▪You can use the pretrained weights or train your own.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/jaredpalmer/kev/tree/main |
| Publication time | Mon, 21 Sep 2026 07:11:55 +0000 |
| Retrieval time | 2026-09-21T07:43:48.281Z |
| Last seen | 2026-09-21T07:43:48.281Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | BVVLquVmG1rn · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Kev Small Jev-like decision models you can train and run yourself. Kev is a family of small decision models built on Qwen3.5 and based on the architecture described in Jev's Architecture Unmasked. You can use the pretrained weights or train your own. The API matches TypeSafe's System One, so you can point their Python SDK at your local server. Highlights 0.8B, 4B, and 9B models, with training code and evaluation data. Yes/no (noul), multiple-choice (choice), and rating (score) questions in the same request. Questions share the input text but can't read each other. Runs on CUDA and Apple Silicon. The 4B and 9B models fit a 32 GB Mac using bf16; see Serving Performance for what to expect on a Mac. A web playground for trying your own inputs and checking how option order affects the answers.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.