Cognica-PoE-v1.0-1.3B-stage-math
Copyright (c) 2026 Cognica, Inc.

This product is licensed under the Apache License, Version 2.0.

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This Work is a dual-head SFT specialist stage trained on top of
Cognica-PoE-v1.0-1.3B-base (Apache 2.0) and incorporates the nanochat
framework:

    nanochat
    Copyright (c) 2025 Andrej Karpathy
    Licensed under the MIT License.
    https://github.com/karpathy/nanochat

The full text of the upstream MIT License is reproduced below for
compliance with its attribution requirement:

    Permission is hereby granted, free of charge, to any person obtaining
    a copy of this software and associated documentation files (the
    "Software"), to deal in the Software without restriction, including
    without limitation the rights to use, copy, modify, merge, publish,
    distribute, sublicense, and/or sell copies of the Software, and to
    permit persons to whom the Software is furnished to do so, subject to
    the following conditions:

    The above copyright notice and this permission notice shall be
    included in all copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
    CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
    TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Training datasets used to produce this stage (each distributed under its
own license — downstream users should consult each dataset card):

    GSM8K (openai/gsm8k, main/train split x20 epochs)
    Copyright (c) OpenAI
    Licensed under the MIT License.
    https://huggingface.co/datasets/openai/gsm8k

    MathInstruct (TIGER-Lab/MathInstruct, x4 epochs)
    Copyright (c) TIGER-Lab contributors
    https://huggingface.co/datasets/TIGER-Lab/MathInstruct

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This Work also uses:

    Muon optimizer (utilized via nanochat's MuonAdamW hybrid)
    Copyright (c) Keller Jordan et al.
    https://github.com/KellerJordan/Muon

Each component is distributed under its original license.