| --- |
| license: apache-2.0 |
| library_name: onnx |
| base_model: convaiinnovations/laya |
| tags: [laya, onnx, system-one, calibrated-decisions, ruby, decision-model] |
| --- |
| |
| # Laya, exported to ONNX |
|
|
| ONNX exports of the three [Laya](https://github.com/NandhaKishorM/laya) checkpoints, published for |
| [ruby-laya](https://github.com/codenamev/ruby-laya). Laya is a non-autoregressive System 1 decision |
| engine by [Convai Innovations](https://huggingface.co/convaiinnovations): typed decisions over any |
| state in a single forward pass, with calibrated probabilities. |
|
|
| These are conversions, not new models. All credit for the weights belongs to Convai Innovations. |
|
|
| | folder | source | encoder | context | |
| |---|---|---|---| |
| | `english/` | [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya) (root) | ModernBERT-large | 512 | |
| | `multilingual/` | the same repository's `multilingual/` | mmBERT-base | 1024 | |
| | `typed-decisions/` | the same repository's `typed-decisions/` | ModernBERT-large | 1024 | |
|
|
| Source revision: `convaiinnovations/laya` at commit `1c5edc17a7acd8701df6fc341c0d179f1c62c982`, exported with `laya` 0.3.7 and |
| PyTorch 2.14.0. |
|
|
| ## What each folder holds |
|
|
| model.onnx the decision model: encoder, typed decision head and action head |
| rl_agent_config.json copied from the source checkpoint (token budgets, temperatures) |
| onnx_config.json export provenance and the verified deviation from PyTorch |
| tokenizer/ copied from the source checkpoint |
| |
| ## Graph |
|
|
| Inputs are `input_ids`, `attention_mask` (int64 `[batch, seq]`), `marker_pos`, `marker_mask` |
| (int64 and bool `[batch, markers]`) and `qtype` (int64 `[batch]`). Outputs are `logits` |
| `[batch, markers]`, `act_logits` `[batch, 2]` and `last_hidden_state` `[batch, seq, dim]`, which |
| the embedding shortlist uses. ONNX Runtime prunes the graph to the outputs you request, so asking |
| for the first two costs nothing extra. |
|
|
| A question with a single option is padded to two markers with the second masked off, and a batch |
| shorter than eight tokens is padded up to it. Both are what the traced graph expects, and neither |
| changes an answer. |
|
|
| Weights are stored as float16 and cast to float32 in the graph. The published checkpoints are |
| float16 on disk and upstream widens them at load, so the round trip is exact and the download |
| stays the size of the original safetensors. |
|
|
| ## Verification |
|
|
| Every export is checked against upstream `laya` on CPU: same sequence construction, same |
| temperature buckets, same rounding. Each probability, score, confidence and action probability |
| upstream reports is reproduced, and raw logits agree within 2.5e-5. Reproduce with |
| [`tools/export_onnx.py`](https://github.com/codenamev/ruby-laya/blob/main/tools/export_onnx.py): |
|
|
| uv run tools/export_onnx.py <checkpoint-dir> <output-dir> |
| |
| ## Use |
|
|
| gem install ruby-laya |
| |
| require "laya" |
| agent = Laya.load("convaiinnovations/laya") # resolves to english/ here |
| |
| Any ONNX Runtime can load these directly; the gem is a convenience, not a requirement. |
|
|
| ## License |
|
|
| Apache 2.0, inherited from the source checkpoints. Laya was developed by Convai Innovations. |
|
|
