Skip to content

OpenThai-SystemOne

An open Thai + English System One decision model at 0.8B, Apache-2.0: choice with up to 255 options, fractional score, and noul answers in one forward pass over a Qwen3.5-0.8B text tower.

Published by
Community
Author
iapp
Last verified
Oct 2026

Highlights

  • Choice accepts up to 255 options, score takes 2–10 ordered levels, and noul returns p(yes), all from one forward pass.
  • Backbone: Qwen3.5-0.8B-Base's text tower, continued-pretrained on about 5B Thai tokens after the vision encoder was removed.
  • A 256-way slot head replaces the LM head; slot 255 is abstain, and slots beyond the option count are masked out.
  • Trained on about 2–3M decision examples with option-order shuffling and abstain examples, then temperature-calibrated with Brier loss.
  • Ships an Ollama build and GGUF that serve the same contract at localhost:11434/v1/systemone with no API key.

Watch out

Apache-2.0. The request and response contract mirrors TypeSafe's POST /v1/systemone so TypeSafe SDK code can point at it unchanged. Through Ollama the 256-slot head does not run: each question is asked as a chat prompt and read from A–Z letter probabilities, which caps questions at 26 options and drops the abstain answer and order-invariant mode.

Questions about this entry

Short answers drawn from the linked project or page.

Reactions & coverage

Posts, threads, and videos about this entry from around the web.

LinkedIn: What multimodal decision models actually change, and what they do not.

LinkedInJevDirectory.org

Hacker News: Pointer to a DiffusionGemma plus vLLM tutorial for a Jev-shaped classifier.

1 pointHow to build a Jev-style classifier with DiffusionGemma and vLLMRead the thread on Hacker News

Related terms

Glossary definitions related to this entry.

More Jev resources like this

An unofficial cross-encoder fine-tune of ModernBERT-ja-310m for Jev Local: it reads a question-plus-state and one candidate per sequence and hits 92.62% on the JGLUE JNLI test, with CC BY-SA 4.0 weights.
Jev Models & Reimplementations#community#open-models#local
Community
The 322M mmBERT-base build of Laya for 100+ languages: state plus typed questions return typed answers with probabilities in one forward pass, with up to 8,192 tokens for long documents.
Jev Models & Reimplementations#community#open-models#local
Community
An Open Classification Foundation smoke checkpoint: a 164M-parameter ModernBERT-base with a cross-entropy decision head, 0.751 accuracy and 0.044 ECE on 2,000 held-out typed decisions.
Jev Models & Reimplementations#community#open-models#local
Community
A 4B decision model taking text, JSON, or images up to 262,144 tokens: 52.75 on Decision Index 0.2.1, 204 of 231 on JevBench public, and 26.0 ms for a three-question request on one H200.
Jev Models & Reimplementations#community#open-models#local
Community
A decision model that scores its own clarifying questions by value of information: at AUC .799 against an oracle .797, it asks only when asking is expected to change the answer, then decides or hands off.
Jev Models & Reimplementations#community#open-models#local
Community
The v3 Jev-Style decision model at 2B in GGUF: F16 3.78 GB and Q8_0 2.01 GB match PyTorch FP32 on 100% and 99.7% of parity rows, 73.6% on JevBench v1.4.1 public items.
Jev Models & Reimplementations#community#open-models#local
Community

From the Jev guides

Original write-ups on what this entry is for.

Training recipes, looped architectures, and language ports: what the open side of the typed-decision class shipped this month, and how to pick by runtime and calibration instead of the leaderboard.
Jev Models & Reimplementations#open-models#training#local
Read article
Taking a decision checkpoint out of Python: what the GGUF and browser WebGPU exports preserve, what they change, and the parity evidence the model cards publish.
Jev Tools & CLIs#open-models#local#onnx
Read article
Interface, size, license, claims — in that order. A selection guide across the system-one catalog, from a 706K form scorer to 27B Jev students.
Jev Practices & Patterns#open-models#evaluation#benchmarks
Read article
Back to all resources

From the Jev community

Posts from builders shipping with Jev right now.

Follow @typesafeai