Experimental 350M typed decision model: shared-cache inference, local WebGPU SDK, reproducible training and honest evaluations. MIT source.
See the codeSee what people are saying (1)
README
Decision Lab
A small model that scores decisions and returns typed JSON, without generating output token by token.
An open research experiment built on a fine-tuned 350M model. It runs locally in a WebGPU browser or Python. Inspired by Jev's typed decision interface; independently implemented and unaffiliated with Jev or TypeSafe.
Live playground · Run locally · How it works · Results · SDK · Contribute · Documentation
How it works
- Turn each field into a question with a fixed set of allowed answers.
- Process the shared context once and cache the model's state.
- Score each question's answers while reusing that cache.
- Convert scores to probabilities and assemble JSON in code.
flowchart LR
A[Context] --> B[Shared cache]
B --> C[Field scores]
C --> D[Typed JSON]
The runtime supports Choice (a label), Noul (P(yes)), and Score (an expected ordinal index). It uses one prefill plus question processing, not one total forward pass. Valid JSON does not guarantee correct decisions. Detailed architecture · Short explanation to share
Try it
Open decision-lab.loomens.com for the complete browser playground. It includes editable Jev-shaped requests, Choice, Noul and Score visualizations, raw JSON, an on-device speed benchmark, numerical parity checks, and the frozen workflow evaluation suite. Model inference runs in your browser; prompts are not sent to a server.
To run it yourself, use Node.js 20+, Python 3.12+, and a browser with WebGPU and
shader-f16:
git clone https://github.com/khalilelghoul01/decision-lab.git
cd decision-lab
python3 scripts/download_model.py
npm --prefix sdk ci --ignore-scripts
npm --prefix sdk run build
npm --prefix browser ci --ignore-scripts
npm --prefix browser run dev
Open the printed URL. /diagnostics.html exposes the low-level runtime checks
and /sdk.html shows the model-loader lifecycle. Prompts stay local;
the first load includes roughly 296 MB of weights and shader compilation.
Prefer a ready-built demo? Download decision-lab-browser-demo.zip from
Releases,
extract it, and run python3 -m http.server 8000 --bind 127.0.0.1 inside the folder.
Open http://localhost:8000/sdk.html.
Current results
This is a concept and experiment, with substantial accuracy limits.
| Measurement | Released INT4 model |
|---|---|
| Held-out public tasks, 1,600 examples | 83.0% accuracy |
| Known workflow suite, 240 fields / 84 records | 59.6% fields / 19.0% exact records |
| Warm four-field browser request, one / two option orders | 92 / 148 ms |
Timing is the median of five warm runs on an M4 Mac mini with 16 GB, excluding loading. Public-task accuracy was measured on ONNX CPU; workflow accuracy on actual WebGPU. The workflow suite is a known regression set, not a blind test. Full methodology, baselines and failures.
The current limit is 32 questions, 2–8 choices per question, and 1,024 tokens per complete field prompt. Fields are independent and can contradict each other. Confidence is not a guarantee of correctness. Training uses supervised cross-entropy plus calibration; this release does not implement RLCD.
Find your way around
| Directory | Purpose |
|---|---|
| sdk/ | TypeScript model loader and typed API |
| browser/ | Playground and SDK demo |
| python/ | Native runtime, training, export and tests |
| docs/ | Architecture, results, dataset and training guides |
| research/ | Frozen evaluation evidence and regression fixtures |
| examples/ | Sample requests and schemas |
| scripts/ | Verified model downloads and release tools |
Independent evaluation cases, better training data, calibration, and browser performance work are welcome. Contribution guide.
License
Original source is MIT. Model weights retain the separate LFM Open License; public datasets retain their upstream terms. Attribution and notices.
Contributors
7 commits
Similar repos
Languages
Python
56.5%
TypeScript
37.6%
JavaScript
3.4%
CSS
1.7%
Experimental 350M typed decision model: shared-cache inference, local WebGPU SDK, reproducible training and honest evaluations. MIT source.
See the codeSee what people are saying (1)
README
Decision Lab
A small model that scores decisions and returns typed JSON, without generating output token by token.
An open research experiment built on a fine-tuned 350M model. It runs locally in a WebGPU browser or Python. Inspired by Jev's typed decision interface; independently implemented and unaffiliated with Jev or TypeSafe.
Live playground · Run locally · How it works · Results · SDK · Contribute · Documentation
How it works
- Turn each field into a question with a fixed set of allowed answers.
- Process the shared context once and cache the model's state.
- Score each question's answers while reusing that cache.
- Convert scores to probabilities and assemble JSON in code.
flowchart LR
A[Context] --> B[Shared cache]
B --> C[Field scores]
C --> D[Typed JSON]
The runtime supports Choice (a label), Noul (P(yes)), and Score (an expected ordinal index). It uses one prefill plus question processing, not one total forward pass. Valid JSON does not guarantee correct decisions. Detailed architecture · Short explanation to share
Try it
Open decision-lab.loomens.com for the complete browser playground. It includes editable Jev-shaped requests, Choice, Noul and Score visualizations, raw JSON, an on-device speed benchmark, numerical parity checks, and the frozen workflow evaluation suite. Model inference runs in your browser; prompts are not sent to a server.
To run it yourself, use Node.js 20+, Python 3.12+, and a browser with WebGPU and
shader-f16:
git clone https://github.com/khalilelghoul01/decision-lab.git
cd decision-lab
python3 scripts/download_model.py
npm --prefix sdk ci --ignore-scripts
npm --prefix sdk run build
npm --prefix browser ci --ignore-scripts
npm --prefix browser run dev
Open the printed URL. /diagnostics.html exposes the low-level runtime checks
and /sdk.html shows the model-loader lifecycle. Prompts stay local;
the first load includes roughly 296 MB of weights and shader compilation.
Prefer a ready-built demo? Download decision-lab-browser-demo.zip from
Releases,
extract it, and run python3 -m http.server 8000 --bind 127.0.0.1 inside the folder.
Open http://localhost:8000/sdk.html.
Current results
This is a concept and experiment, with substantial accuracy limits.
| Measurement | Released INT4 model |
|---|---|
| Held-out public tasks, 1,600 examples | 83.0% accuracy |
| Known workflow suite, 240 fields / 84 records | 59.6% fields / 19.0% exact records |
| Warm four-field browser request, one / two option orders | 92 / 148 ms |
Timing is the median of five warm runs on an M4 Mac mini with 16 GB, excluding loading. Public-task accuracy was measured on ONNX CPU; workflow accuracy on actual WebGPU. The workflow suite is a known regression set, not a blind test. Full methodology, baselines and failures.
The current limit is 32 questions, 2–8 choices per question, and 1,024 tokens per complete field prompt. Fields are independent and can contradict each other. Confidence is not a guarantee of correctness. Training uses supervised cross-entropy plus calibration; this release does not implement RLCD.
Find your way around
| Directory | Purpose |
|---|---|
| sdk/ | TypeScript model loader and typed API |
| browser/ | Playground and SDK demo |
| python/ | Native runtime, training, export and tests |
| docs/ | Architecture, results, dataset and training guides |
| research/ | Frozen evaluation evidence and regression fixtures |
| examples/ | Sample requests and schemas |
| scripts/ | Verified model downloads and release tools |
Independent evaluation cases, better training data, calibration, and browser performance work are welcome. Contribution guide.
License
Original source is MIT. Model weights retain the separate LFM Open License; public datasets retain their upstream terms. Attribution and notices.
Similar repos
Contributors
7 commits
Languages
Python
56.5%
TypeScript
37.6%
JavaScript
3.4%
CSS
1.7%