Models › NanoJev family

C-Tianyu Not declared out 38 days ago

Verified 2026-10-25 Rank #0 in Decision models · score 0 method v1 Sources 3 Downloads 30d 2.4k · #575 of 619 tracked

Can I use it?Not declaredNo license declared on the model cardlicence file2026-10-24
Do people use it?2.4kdownloads, 30 days#575 of 619 we track · below the median+0 likes, downloads −74% this week88 likesHugging Face2026-10-24

Worth reading

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Latest · 33 days ago

Nanojev: A minimal Jev-style decision model in 200 loc

nanojev is a hypothetical reconstruction of TypeSafe's closed Jev decision model, designed as the smallest working version of what black-box probing suggests.

github.com 2026-09-22 2 HN pts 290 words

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 5 Spaces.

  • As a service0no API provider listed
  • On own hardware0quantized builds
  • Built on66 fine-tunes · 0 adapters · 0 merges
  • In apps5Hugging Face Spaces

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 4 repo commits so far

Teal is the weights (2026-09-17), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

Oct2026-09-20: 1 repo commit2026-09-17: 3 repo commitsNanojev: A minimal Jev-style decision model in 200 loc · 2026-09-22Weights 2026-09-17 · 2026-09-17Weights 2026-09-17now
Oct2026-09-20: 1 repo commit2026-09-17: 3 repo commitsNanojev: A minimal Jev-style decision model in 200 loc · 2026-09-22Weights 2026-09-17 · 2026-09-17Weights 2026-09-17now

What people built on it

6 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

6 derivatives on the Hub, most of them fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

6 fine-tunes (Hugging Face model tree)6 fine-tunes
6 fine-tunes (Hugging Face model tree)6 fine-tunes

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

4 rows

Benchmarks 4 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (4)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported Basic128/128—model author2026-10-07
author-reported Maze4/10—model author2026-10-07
author-reported Predict Position27/128—model author2026-10-07
author-reported Snake8/8—model author2026-10-07

What the card says

3 cited sentences

Summary generated, cited

NanoJev is a 0.6B decision model fine-tuned from the Qwen3-0.6B backbone.[1][2] The model returns action distributions for tasks including Maze, Snake, and ViZDoom Basic.[1] It utilizes a shared backbone with an attention-based Choice head to score candidate sets.[3]

  1. “One 0.6B model returns action distributions for Maze, Sna…”
  2. base models
  3. “A shared Qwen3-0.6B backbone, end-of-sequence representat…”

Generated 2026-10-07 15:19 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

2 use cases

What it is good at 2 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

10 fields

Listed in Decision models

Facts

Librarypytorch
MoEno
Languagesen
LicenseNot declared
Hub snapshot2026-10-25

Decision facts

catalog

Decision-model catalog curated

How it was madedecision head on Qwen3-0.6B
Lineage bucketF - fine-tune of an open model (public/synthetic data)
Lineage evidenceHF base_model + tracker
Base modelQwen/Qwen3-0.6B
Hosted price (input)self-hosted/M
Sourcehttps://huggingface.co/C-Tianyu/NanoJev

All variants

6 variants

Lineage

Fine-tuned from Qwen/Qwen3-0.6B Family: C-Tianyu/NanoJev 6 derivatives on the Hub

Base model: Qwen/Qwen3-0.6B

finetune: 6

Top variants (6 shown, ranked by downloads)

Quant
6 rows
Variant Relation Quant Params Band License Author Likes Downloads
zerodegress/NanoJev-mlx-4bit finetune mlx — — — zerodegress 1 575
zerodegress/NanoJev-bf16 finetune -bf16 — — — zerodegress 0 133
candypunk/NanoJev-Web finetune — — — other candypunk 1 46
yannelli/nanojev-code-review-decisions finetune — — — apache-2.0 yannelli 1 40
FluidInference/nanojev-coreml finetune — — — — FluidInference 0 13
jayzou3773/ra2-nanojev-sft-epoch2 finetune — — — — jayzou3773 0 13

Repository changelog

4 commits

Repository changelog 4 commits

DateCommitSHA
2026-09-20 Publish unified game model and complete reproducibility package 047b927
2026-09-17 Add maze, Snake, and calibrated-decision checkpoints 4a19595
2026-09-17 Upload NanoJev checkpoints and model card 3572bcb
2026-09-17 initial commit dc2466d

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Analysis1

github.com2026-09-22authorityHN 2 pts · 0 comments290 words
nanojev is a hypothetical reconstruction of TypeSafe's closed Jev decision model, designed as the smallest working version of what black-box probing suggests. It uses Qwen3.5-4B-Base + LoRA with a pointer head and trains on 6,191 requests over 55 domains. Inference answers twenty questions on one state in 160 ms forked or 1.9 s as rows.written, reviewed, cited

Similar models

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:44 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:19 UTC · Last verified 2026-10-25

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