README

Vis Decisions

Train Laya and GLiNER decision models with one Python environment and one Vis extension. Keep your training rows local. Export verified FP32 inference bundles for Vis.

When to use

• To train from chat, enable the extension.
• To train from Python, open a complete checkpoint.
• To continue an interrupted run, resume a saved checkpoint.
• To select a model or configure live decisions, use the Vis decision-model guide.

Use in Vis

Add the extension to your project vis.yml:

extensions:
  vis-decisions:
    source: https://github.com/Blockether/vis-decisions
    version: '0.1.0'

Reload the project. The first installation downloads the pinned Python dependencies. Local training needs Python 3.12 or newer, enough memory, and space for complete model checkpoints. Decide-1B needs several gigabytes for each checkpoint or export.

Ask Vis to show the decision models and their download sizes. Then ask it to train a selected checkpoint with your training and evaluation files. Give it a new output directory and a quality policy.

The extension exposes these tools:

Training, export and packaging do not publish a model or activate an alias. Review the validation report before importing the model into a gateway.

Train with Python

Clone this repository. Install the locked environment:

uv sync --frozen --python 3.12

The same environment trains both families. It pins Transformers 5.17.0, GLiNER2 2.0.0, Laya 0.3.22 and PyTorch 2.14.0. The model releases contain weights and metadata, not Python dependency archives.

Find a pinned model revision with decisions.models() or the Vis model catalog. Download the complete training checkpoint with TrainingBundle.fetch(model_ref, destination). This explicit call downloads model assets. Local checkpoint loading does not access the network.

from blockether.vis_decisions import Trainer, TrainingBundle

checkpoint = TrainingBundle.open("./models/training")
with Trainer(checkpoint) as trainer:
    result = trainer.train(
        train_data="./data/train.jsonl",
        eval_data="./data/eval.jsonl",
        training_config="./data/training.json",
        validation_policy="./data/quality.json",
        output_dir="./runs/customer-support-v1",
        progress=print,
    )
print(result.validation_report)

Use complete checkpoints, not encoder-only weights or ONNX inference bundles. The loader verifies each file and selects the family from model provenance. Training rows and evaluation rows must be disjoint. Keep the output directory new for each run.

The Vis guide explains the row formats, training settings and quality policy. The Python package also provides Decisions(gateway) for explicit gateway import and activation.

Resume or continue training

Set checkpoint_steps in the training configuration to save partial checkpoints. Stop after a checkpoint has been saved. Reopen the saved checkpoint directory. Call Trainer.train with the same ordered rows and settings, using a new output directory.

A matching partial run resumes its saved step and batch order. The optimizer is recreated, so this is not an exact optimizer-state continuation. Changed rows or settings start a new run from the saved weights. A completed checkpoint can start another fine-tuning run on new rows.

Model assets and precision

Model assets remain in Vis assets-pack. The canonical catalog is maintained in Vis and published as decisions.json. This repository does not keep a second catalog.

Decide-1B FP32 and training archives exceed GitHub's per-file limit. They use ordered parts of at most 2,000,000,000 bytes. The downloader verifies every part, joins them, and verifies the complete archive before extraction. No conversion to FP16, BF16 or quantized weights is used to reduce their size.

Development

uv sync --frozen --python 3.12
uv run ruff format --check .
uv run ruff check .
uv run python -m pytest -q
uv build

Most tests do not download model weights. To run the offline Laya acceptance tests, set VIS_LAYA_TRAINING_DIR to a verified complete checkpoint. To run the Decide-1B acceptance test, set VIS_GLINER_DECIDE_1B_CHECKPOINT and VIS_GLINER_LICENSE. The real-model tests cover train, stop, resume, export and reopening in a new process.