chand1012/laya-instinct

Laya Instinct

Laya Instinct is a fine-tuned Laya decision model for choice, score, and noul (yes/no) questions. It is a full checkpoint, not an adapter. The weights are released under Apache-2.0, the same license declared by the base model.

Training data and attribution

The 500,000-record training mixture was constructed from the following Hugging Face datasets. Counts are records in the local training split, not the total size of each upstream repository.

Source dataset	Training records	Contribution
tasksource/tasksource-jev-typed-decisions	221,689	Direct normalized records
SargeDev/jev-distill-corpus-v3	193,978	Human/teacher decision targets; exact-uniform yuri_v1 placeholders excluded
Praveenrajus/jev-bench	55,422	Normalized benchmark records
LocalLLaMA/typed-decisions	1,200	Typed decision workflows
Tasksource-derived robustness augmentation	27,711	Option-order perturbations of Tasksource records, not an additional upstream dataset

The mixture contains 504,800 individual decision questions. A separate 6,000-record calibration split was used to fit per-decision-type temperatures. The validation split was excluded from optimization and calibration. The dataset builder and its local manifest.json record the construction settings; upstream test/OOD splits were excluded.

Data licensing: Apache-2.0 here describes the released model weights; it does not relicense the training data. The Tasksource collection has source-specific licenses and usage terms, including commercial/non-commercial/unspecified labels. jev-bench aggregates datasets with their own licenses (for example, ARC rows identify CC-BY-SA-4.0). Review the linked dataset cards and original source terms before using this model or reconstructing the mixture for a particular purpose. The other two linked datasets declare Apache-2.0 on Hugging Face.

Training and evaluation

Starting from convaiinnovations/laya, the model was trained for four epochs on one RTX 3090 Ti, with effective batch size 64 and maximum sequence/head lengths of 1024/256. The final checkpoint uses calibrated temperatures for choice, score, and noul decisions, respectively: 4.1300, 2.5513, and 3.4845.

On a 6,000-record held-out validation split from the same source families, evaluated with a common 1024/256 inference budget:

Model	Accuracy	Brier score	Log loss
Plain Laya	43.48%	0.5714	1.7356
Laya typed-decisions	43.78%	0.5010	1.3870
Laya Instinct	71.25%	0.3442	1.0567

This is an in-domain validation result, not an independent downstream benchmark. High-cardinality classification remains weak (Banking77: 5%; CLINC150: 1%). The two official checkpoint comparators emitted a temperature-bucket warning, so their calibration-error values are not directly comparable; accuracy and the metrics above are reported under the same inference budget.

Use

Install the Laya package and load this checkpoint with laya.Agent("chand1012/laya-instinct", device="cuda"). The repository contains model.safetensors, rl_agent_config.json, encoder configuration, and tokenizer files. It is not a standard generative language model.

Reproducibility

• Model file SHA-256: 584c72a0f320c2884deacba31fb4d4378f548f264549bde841086a02857248b6
• Mixture manifest SHA-256: f1f89efa5d6572c47a8bae752ed2f93738f83c962c9f258b1d09f4bcab1530b7
• Training seed: 20260922; dataset construction seed: 20260926