--- title: laya-typed-decisions canonical_url: "https://www.modelscope.cn/models/convaiinnovations/laya-typed-decisions" md_url: "https://www.modelscope.cn/models/convaiinnovations/laya-typed-decisions.md" repository: convaiinnovations/laya-typed-decisions last_updated: 2026-10-04 license: apache-2.0 pipeline_tag: text-classification tasks: - text-classification model_type: - laya architectures: - LayaTypedDecisions parameters: 421.3M tensor_type: - F16 library_name: - safetensors - pytorch frameworks: - pytorch language: - en downloads: 242 stars: 7 tags: - laya - system-one - calibrated-decisions - rlcd - typed-decisions - classification - agent-observability - invoice-processing - security-incidents - customer-service - commercial-use --- # laya-typed-decisions > laya-typed-decisions - convaiinnovations 在 ModelScope 开源的模型。Laya Typed-Decisions convaiinnovations/laya-typed-decisions 是 ModelScope 魔搭社区上的 421.3M 参数text-classification模型，采用 apache-2.0 许可。 - **Repository**: convaiinnovations/laya-typed-decisions - **License**: apache-2.0 - **Tasks**: text-classification - **Parameters**: 421.3M - **Tags**: laya, system-one, calibrated-decisions, rlcd, typed-decisions, classification, agent-observability, invoice-processing, security-incidents, customer-service, commercial-use - **Downloads**: 242 - **Stars**: 7 - **Last updated**: 2026-10-04 Source: https://www.modelscope.cn/models/convaiinnovations/laya-typed-decisions ---

# Laya Typed-Decisions Non-autoregressive **System 1 decision model**, fine-tuned on the [typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) workflows: agent-trace observability, customer service, invoice processing and security incidents. Part of the [Laya family](https://huggingface.co/convaiinnovations/laya). | checkpoint | encoder | params | context | use it for | |---|---|---|---|---| | [`convaiinnovations/laya`](https://huggingface.co/convaiinnovations/laya) | ModernBERT-large | 421M | 512 | English, general | | [`convaiinnovations/laya-multilingual`](https://huggingface.co/convaiinnovations/laya-multilingual) | mmBERT-base | 322M | 1024 | 100+ languages | | **`convaiinnovations/laya-typed-decisions`** (this repo) | ModernBERT-large | 421M | 1024 | these four workflows | ## Benchmark 400 test cases, 2,000 decisions, measured on the official test split. | model | accuracy | soft acc | Brier | ECE | score MAE | |---|---|---|---|---|---| | **this checkpoint** | **0.766** | 0.471 | **0.062** | 0.213 | **0.242** | | *TypeSafe Jev 1.13.0 (published)* | *0.727* | *0.580* | *0.148* | *0.144* | *0.391* | | *teacher self-agreement ceiling* | *0.735* | | | | | | *ModernBERT-base specialist (published)* | *0.646* | | | | | | *per-question majority class* | *0.461* | | | | | | *random guess* | *0.318* | | | | | | `laya` (not fine-tuned) | 0.362 | 0.332 | 0.316 | 0.175 | 0.694 | | `laya-multilingual` (not fine-tuned) | 0.342 | 0.326 | 0.439 | 0.285 | 0.687 | **+3.9 points over Jev's published 0.727, above the 0.735 teacher ceiling**, with 2.4x better Brier and 1.6x better score MAE. Jev figures are third-party published, not measured here — there is no TypeSafe API access in this project, and sample sizes and prompts differ. Treat the comparison as indicative. ### By workflow | workflow | accuracy | |---|---| | invoice processing | **0.804** | | security incidents | 0.766 | | customer service | 0.764 | | agent-trace observability | 0.730 | ### By primitive | type | accuracy | ECE | n | |---|---|---|---| | `noul` | **0.857** | 0.192 | 600 | | `choice` | 0.733 | 0.255 | 600 | | `score` | 0.723 | 0.199 | 800 | ## Quickstart ```bash pip install laya ``` ```python import laya agent = laya.load("convaiinnovations/laya-typed-decisions") result = agent.predict(state, questions) ``` Or route to it explicitly: ```python from laya import Router router = Router() router.predict(state, questions, model="typed-decisions") ``` `Router` will not select this checkpoint automatically unless you construct it with `auto_task_detection=True` — it is specialised to four synthetic workflows and should not be a silent default. If this checkpoint is on a hot path, keep it resident rather than loading it per request: ```python router = Router() router.preload(["typed-decisions"]) # or router.attach("typed-decisions", agent) ``` `preload` fetches and builds the checkpoints you name once; `attach` registers an `Agent` you already hold, so nothing is loaded twice. `laya.load()` builds the model without a throwaway random initialisation, so loading is about 10x faster with identical answers. `agent.predict_batch(states, questions)` scores many states in shared forward passes, with answers identical to one call per state, which suits workflow triage over a queue. > **If `laya.load()` hangs:** `transformers` probes for TensorFlow at import, and when TF is > installed its abseil runtime can deadlock model construction. Run with `USE_TF=0`. ## Training Fine-tuned from [`convaiinnovations/laya`](https://huggingface.co/convaiinnovations/laya) on the benchmark's 1,200-case training split (6,000 decisions) with **RLCD**: the policy reports a distribution, exploration adds zero-mean Gaussian noise to the logits, and the reward is a strictly proper scoring rule (log + spherical, plus ranked probability score for ordinal questions), so expected reward is maximised only by honest probabilities. Updates are REINFORCE with a group-mean baseline, alongside soft cross-entropy against the teacher's distributions. Reproduce it: [`laya_finetune_typed_decisions_2xT4_kaggle.ipynb`](https://github.com/NandhaKishorM/laya/blob/main/notebooks/laya_finetune_typed_decisions_2xT4_kaggle.ipynb) — about 4–5 hours on Kaggle's free 2xT4. ## Limits - **This is a specialist.** It was fine-tuned on four specific synthetic workflows. Expect it to behave like the base `laya` checkpoint, or worse, on anything else. - **Soft accuracy trails Jev** (0.471 vs 0.580): its argmax is better, but its probability *distributions* match the teacher less well. - **Still over-confident** (ECE 0.213 vs Jev's 0.144). Its `temperature_by_options` was inherited from the base checkpoint and overrides the per-type temperatures fitted for this model — refit on your own held-out data before relying on the probabilities. - **Its per-type temperatures were fitted on training data.** The fine-tuning run fitted `[1.0148, 1.0374, 1.0575]` on a slice of the same items it had just trained on, which is why they sit so close to 1.0 ([#186](https://github.com/NandhaKishorM/laya/issues/186)). The notebook now holds that slice out of training. Until this checkpoint is refit, treat its confidence as uncalibrated. - English only. Use `laya-multilingual` for other languages. - **Keep `choice` questions under ~20 options.** Options share a fixed 256-token head budget, so a large label space leaves few tokens per label and accuracy falls off sharply. ## Links - **Docs** https://nandhakishorm.github.io/laya/ - **Hub / family** https://huggingface.co/convaiinnovations/laya - **GitHub** https://github.com/NandhaKishorM/laya · full benchmark data on the `research` branch - **PyPI** https://pypi.org/project/laya/ - **Benchmark dataset** https://huggingface.co/datasets/LocalLLaMA/typed-decisions Apache 2.0 · Convai Innovations