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Copyright 2026 MainStack
This product is licensed under the Apache License, Version 2.0 (the "License").
You may obtain a copy of the License in the accompanying LICENSE file or at:
http://www.apache.org/licenses/LICENSE-2.0
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Attribution request (downstream use)
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marvy-1-14B was created by MainStack (https://huggingface.co/MainStack).
If you use marvy-1-14B as a baseline, fine-tune it, distill from it, evaluate
against it, or otherwise build on it, please credit MainStack and link to:
https://huggingface.co/MainStack/marvy-1-14B
Under the Apache License, Version 2.0, this NOTICE file MUST be retained and
reproduced in any derivative works and redistributions (License §4(d)).
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Dual licensing
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* Model weights (safetensors / GGUF / LoRA adapter): Apache-2.0 (LICENSE).
* MainStack original contributions — model cards, documentation, benchmark,
charts, and curated training methodology: CC-BY-4.0 (LICENSE-CC-BY-4.0).
Reuse of MainStack's contributions requires attribution to MainStack under the
terms of CC-BY-4.0. See LICENSING.md for the full breakdown.
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Attribution
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marvy-1-14B is a fine-tuned derivative of:
Qwen2.5-14B-Instruct
Copyright Alibaba Cloud / Qwen Team
Licensed under the Apache License, Version 2.0
https://huggingface.co/Qwen/Qwen2.5-14B-Instruct
The base model weights are the property of their respective authors and are
used and redistributed in modified (fine-tuned) form under the terms of the
Apache License, Version 2.0.
Citation for the base model:
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
author = {Qwen Team},
year = {2024},
url = {https://qwenlm.github.io/blog/qwen2.5/}
}
@article{qwen2,
title = {Qwen2 Technical Report},
author = {Qwen Team},
journal= {arXiv preprint arXiv:2407.10671},
year = {2024}
}
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Tooling
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Trained and fused with MLX-LM (https://github.com/ml-explore/mlx-lm),
Copyright Apple Inc., licensed under the MIT License.
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Training data provenance
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marvy-1-14B was fine-tuned on a corpus of anonymized ServiceNow delivery
artifacts. All customer and partner names were replaced with stable aliases,
and emails, hostnames, IP addresses, and credential-bearing files were removed
or redacted prior to training. No customer-identifying information is present
in the training corpus. See the model card for the full redaction methodology.
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