Training requirements

Required files

File	Requirement
Model .wasm	A trusted model prepared for Weei WASM ABI 1. A PyTorch, ONNX, or Python file cannot be used directly.
Model .wgsl	Optional shader archive from the matching model package. Requires its WASM model; enables GPU training when supported.
Manifest .json	A model package manifest.json, required for experience jobs, or a dataset chunk array with id, fileName, bytes, sha256, format and exampleCount.
Dataset files	For dataset jobs and experience models with dataset inputs: every named chunk, readable independently. Not required for inputs: none. Include any declared auxiliary weight files.
Validation file	Optional for dataset jobs. Separate evaluation data, kept on the coordinator. Experience models without inputs use generated evaluation instead.
.weei-checkpoint	Optional. A saved model state for continued training with a compatible model and the required data files.

Dataset manifest

[{
  "id": "chunk-001",
  "fileName": "training-001.bin",
  "bytes": 4096,
  "sha256": "REPLACE_WITH_REAL_64_CHARACTER_SHA256",
  "format": "MODEL_SUPPORTED_FORMAT",
  "exampleCount": 128
}]

Replace the sample values with the actual file metadata. File names, byte sizes, and SHA-256 checksums must match. Obtain example counts and format identifiers from your model's data preparation tools.

Preparation

1. Obtain a compatible model and its data preparation instructions.
2. For dataset jobs, prepare self-contained chunks and a matching manifest. For RL, use the experience model package described below.
3. Reserve separate validation examples if the model supports evaluation.
4. Select all files in the workspace, configure settings, and create the job.
5. Start with a small dataset before committing to a longer run.

Devices and participants

The site origin must be registered with Weei and training must be available to the user. Keep the coordinator's browser open for distributing data, collecting results, and saving checkpoints. Other participants must accept invitations before receiving model and data files.

Training chunks are processed on participant devices. Share data only with appropriate participants. Save important checkpoints outside browser storage.

CPU and GPU files

WASM is required for every job. Add the model package’s WGSL shader archive to allow GPU training. Without WGSL, training uses CPU. WGSL cannot replace the WASM model. The optional archive is passed as executionFiles for both new jobs and checkpoint continuation.

Reinforcement learning: required package

The model supplies the environment, policy and learning algorithm. Weei distributes experience assignments and aggregates updated weights. Select model.wasm and its manifest.json in weei-model-package format. A plain chunk list is not sufficient for an experience model. The package must declare workSource and any auxiliaryWeights.

Package declaration	What to provide
workSource.kind: experience; inputs: none	No dataset chunks. Select auxiliary weight files if declared. The environment generates experience inside the model.
inputs: dataset	All input chunks, for example prompts, and auxiliary files. The model must declare workPartitioning so input records are not split.
auxiliaryWeights with sha256	A matching file in Training files. Weei verifies its size and SHA-256.
auxiliaryWeights with source	No local file. Weei supplies the initial checkpoint or the checkpoint roundsAgo rounds earlier.

Configure an experience job

1. Select the package manifest. The form changes to Training rounds and the model’s units per assignment, such as episodes.
2. Choose units within the package’s minimum and maximum. Batch size, learning rate and local epochs have model-defined meanings.
3. For generated evaluation, the model needs capabilities.evaluation and a positive workSource.evaluationUnits. Enable Schedule validation with a round interval. Models with inputs: none do not take a validation dataset.
4. Create the job, connect participants, invite them and start. Review training and evaluation metrics by checkpoint round.

When continuing a checkpoint, Training rounds is the final round number: round 397 with a target of 500 trains 103 more rounds. Resume restores the experience sequence; New starts a new sequence while keeping the weights. File-based auxiliary weights must match the checkpoint.

Execution limits

CPU experience assignments without input data can use multiple local Workers when the model declares maxWorkers above one. GPU experience training is hybrid: WASM plays episodes and the GPU runs policy updates. It requires a package-declared GPU backend and the matching shader archive in GPU shaders. Experience training over dataset inputs currently has no GPU support. The model and device determine the actual mode.

Validation files are optional for dataset training. If supplied with a package, the file must match its declared name, size and SHA-256. A model must declare evaluation support.

RL package and model contract ↗