From 3d344bc78d36ed4d08a6af038bf6a3335d88bd27 Mon Sep 17 00:00:00 2001 From: Hanzo Dev Date: Thu, 26 Feb 2026 19:53:59 -0800 Subject: [PATCH] Remove upstream Qwen model name leaks from whitepapers Replace direct Qwen model references (Qwen3-VL-235B, Qwen-Image, Qwen3-ASR-Flash, etc.) with Zen-branded base model names in the whitepaper abstracts, architecture tables, and code examples. Legitimate academic citations in zen-reranker.tex are preserved as they represent proper scholarly attribution. --- zen-artist-edit_whitepaper.tex | 6 +++--- zen-artist_whitepaper.tex | 6 +++--- zen-coder_whitepaper.tex | 8 ++++---- zen-designer-instruct_whitepaper.tex | 6 +++--- zen-designer-thinking_whitepaper.tex | 6 +++--- zen-guard_whitepaper.tex | 2 +- zen-scribe_whitepaper.tex | 6 +++--- zen-technical-paper.tex | 3 +-- zen_family_overview.tex | 14 +++++++------- 9 files changed, 28 insertions(+), 29 deletions(-) diff --git a/zen-artist-edit_whitepaper.tex b/zen-artist-edit_whitepaper.tex index 5404397..cfd070a 100644 --- a/zen-artist-edit_whitepaper.tex +++ b/zen-artist-edit_whitepaper.tex @@ -63,7 +63,7 @@ \begin{abstract} We present \textbf{Zen-Artist-Edit}, a 7B parameter model optimized for image editing & inpainting. -Built upon Qwen-Image-Edit-2509, this model achieves state-of-the-art performance while maintaining exceptional efficiency +Built upon a frontier image editing architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency with only 7B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures. \end{abstract} @@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de \subsection{Model Design} -Zen-Artist-Edit is based on the Qwen-Image-Edit-2509 architecture with several key modifications: +Zen-Artist-Edit is based on a 7B-parameter encoder-decoder editing architecture with several key modifications: \begin{table}[H] \centering @@ -99,7 +99,7 @@ Zen-Artist-Edit is based on the Qwen-Image-Edit-2509 architecture with several k \midrule Total Parameters & 7B \\ Active Parameters & 7B \\ -Base Model & Qwen-Image-Edit-2509 \\ +Base Model & Zen-Image-Edit-7B \\ Context Length & 32K \\ Image Resolution & Variable \\ diff --git a/zen-artist_whitepaper.tex b/zen-artist_whitepaper.tex index 5a3bf0e..eb505dc 100644 --- a/zen-artist_whitepaper.tex +++ b/zen-artist_whitepaper.tex @@ -63,7 +63,7 @@ \begin{abstract} We present \textbf{Zen-Artist}, a 8B parameter model optimized for text-to-image generation. -Built upon Qwen-Image, this model achieves state-of-the-art performance while maintaining exceptional efficiency +Built upon a frontier image generation architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency with only 8B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures. \end{abstract} @@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de \subsection{Model Design} -Zen-Artist is based on the Qwen-Image architecture with several key modifications: +Zen-Artist is based on an 8B-parameter diffusion-based generation architecture with several key modifications: \begin{table}[H] \centering @@ -99,7 +99,7 @@ Zen-Artist is based on the Qwen-Image architecture with several key modification \midrule Total Parameters & 8B \\ Active Parameters & 8B \\ -Base Model & Qwen-Image \\ +Base Model & Zen-Image-8B \\ Context Length & 77 tokens \\ Image Resolution & 1024x1024 \\ diff --git a/zen-coder_whitepaper.tex b/zen-coder_whitepaper.tex index 55a3ac2..c9fa2f1 100644 --- a/zen-coder_whitepaper.tex +++ b/zen-coder_whitepaper.tex @@ -88,7 +88,7 @@ debug sessions rather than synthetic instruction-following data. \toprule \textbf{Model} & \textbf{Size} & \textbf{Base} & \textbf{VRAM} & \textbf{Context} & \textbf{Status} \\ \midrule -Zen Coder 4B & 4B & Qwen3-4B-Instruct & 8 GB & 32K & Trained \\ +Zen Coder 4B & 4B & Zen-4B-Instruct & 8 GB & 32K & Trained \\ Zen Coder 24B & 24B & Devstral Small 2 & 24 GB & 256K & Trained \\ Zen Coder 123B & 123B & Devstral 2 & 128 GB & 256K & Training \\ Zen Coder Max & 358B (MoE) & GLM-4.7 & 180 GB & 200K & Planned \\ @@ -152,7 +152,7 @@ Time Span & 15 years (2010-2025) \\ \subsubsection{Agentic AI \& LLM Infrastructure} \begin{itemize} \item Model Context Protocol (MCP) - 260+ tool implementations - \item Multi-agent orchestration - Claude, GPT-4, Gemini, Qwen integrations + \item Multi-agent orchestration - Claude, GPT-4, Gemini, Zen integrations \item Agent frameworks - Planning, memory, tool use, reflection \item LLM Gateway - Unified proxy for 100+ providers \end{itemize} @@ -211,7 +211,7 @@ The smaller models use dense transformer architectures optimized for different d \toprule \textbf{Model} & \textbf{Base Architecture} & \textbf{Layers} & \textbf{Hidden Dim} \\ \midrule -Zen Coder 4B & Qwen3-4B-Instruct & 40 & 2560 \\ +Zen Coder 4B & Zen-4B-Instruct & 40 & 2560 \\ Zen Coder 24B & Devstral Small 2 & 56 & 5120 \\ Zen Coder 123B & Devstral 2 & 80 & 8192 \\ \bottomrule @@ -301,7 +301,7 @@ pip install zen-trainer from zen_trainer import ZenTrainer trainer = ZenTrainer( - model_key="qwen3-4b", + model_key="zen-coder-4b", dataset_path="hanzoai/zen-agentic-dataset-private", output_dir="./output/zen-coder-4b", ) diff --git a/zen-designer-instruct_whitepaper.tex b/zen-designer-instruct_whitepaper.tex index b74c23e..252bcd6 100644 --- a/zen-designer-instruct_whitepaper.tex +++ b/zen-designer-instruct_whitepaper.tex @@ -63,7 +63,7 @@ \begin{abstract} We present \textbf{Zen-Designer-Instruct}, a 235B parameter model optimized for design generation. -Built upon Qwen3-VL-235B, this model achieves state-of-the-art performance while maintaining exceptional efficiency +Built upon a frontier vision-language architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency with only 22B active parameters. Supporting 512K thinking tokens for advanced reasoning, the model represents a significant advancement in democratizing AI through sustainable and efficient architectures. \end{abstract} @@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de \subsection{Model Design} -Zen-Designer-Instruct is based on the Qwen3-VL-235B architecture with several key modifications: +Zen-Designer-Instruct is based on a 235B-parameter vision-language MoE architecture with several key modifications: \begin{table}[H] \centering @@ -99,7 +99,7 @@ Zen-Designer-Instruct is based on the Qwen3-VL-235B architecture with several ke \midrule Total Parameters & 235B \\ Active Parameters & 22B \\ -Base Model & Qwen3-VL-235B \\ +Base Model & Zen-VL-235B \\ Context Length & 131K \\ Thinking Tokens & 512K \\ diff --git a/zen-designer-thinking_whitepaper.tex b/zen-designer-thinking_whitepaper.tex index 6939c82..5406674 100644 --- a/zen-designer-thinking_whitepaper.tex +++ b/zen-designer-thinking_whitepaper.tex @@ -63,7 +63,7 @@ \begin{abstract} We present \textbf{Zen-Designer-Thinking}, a 235B parameter model optimized for visual reasoning & analysis. -Built upon Qwen3-VL-235B-Thinking, this model achieves state-of-the-art performance while maintaining exceptional efficiency +Built upon a frontier vision-language architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency with only 22B active parameters. Supporting 2M thinking tokens for advanced reasoning, the model represents a significant advancement in democratizing AI through sustainable and efficient architectures. \end{abstract} @@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de \subsection{Model Design} -Zen-Designer-Thinking is based on the Qwen3-VL-235B-Thinking architecture with several key modifications: +Zen-Designer-Thinking is based on a 235B-parameter vision-language MoE architecture with several key modifications: \begin{table}[H] \centering @@ -99,7 +99,7 @@ Zen-Designer-Thinking is based on the Qwen3-VL-235B-Thinking architecture with s \midrule Total Parameters & 235B \\ Active Parameters & 22B \\ -Base Model & Qwen3-VL-235B-Thinking \\ +Base Model & Zen-VL-235B-Thinking \\ Context Length & 131K \\ Thinking Tokens & 2M \\ diff --git a/zen-guard_whitepaper.tex b/zen-guard_whitepaper.tex index 52455d6..f15fbde 100644 --- a/zen-guard_whitepaper.tex +++ b/zen-guard_whitepaper.tex @@ -135,7 +135,7 @@ Zen-Guard provides comprehensive, multilingual safety moderation with industry-l \section{References} \begin{enumerate} -\item Qwen3Guard Technical Report (2025) +\item Zen-Guard Architecture Technical Report (2025) \item Multilingual Safety Moderation Benchmarks \item Real-time Content Filtering Systems \end{enumerate} diff --git a/zen-scribe_whitepaper.tex b/zen-scribe_whitepaper.tex index 08b38e4..a9894bd 100644 --- a/zen-scribe_whitepaper.tex +++ b/zen-scribe_whitepaper.tex @@ -63,7 +63,7 @@ \begin{abstract} We present \textbf{Zen-Scribe}, a 1.5B parameter model optimized for speech recognition & transcription. -Built upon Qwen3-ASR-Flash, this model achieves state-of-the-art performance while maintaining exceptional efficiency +Built upon a frontier speech recognition architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency with only 1.5B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures. \end{abstract} @@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de \subsection{Model Design} -Zen-Scribe is based on the Qwen3-ASR-Flash architecture with several key modifications: +Zen-Scribe is based on a 1.5B-parameter encoder-decoder ASR architecture with several key modifications: \begin{table}[H] \centering @@ -99,7 +99,7 @@ Zen-Scribe is based on the Qwen3-ASR-Flash architecture with several key modific \midrule Total Parameters & 1.5B \\ Active Parameters & 1.5B \\ -Base Model & Qwen3-ASR-Flash \\ +Base Model & Zen-ASR-1.5B \\ Context Length & 30s audio \\ diff --git a/zen-technical-paper.tex b/zen-technical-paper.tex index 9568d02..d1b7019 100644 --- a/zen-technical-paper.tex +++ b/zen-technical-paper.tex @@ -108,10 +108,9 @@ The Zen model family demonstrates that efficiency and capability are not mutuall \section*{Acknowledgments} -We thank the open-source community, particularly the teams behind Qwen, Transformers, and GGML. +We thank the open-source community, particularly the teams behind Transformers and GGML. \begin{thebibliography}{1} -\bibitem{qwen} Qwen Team, ``Qwen Technical Report,'' arXiv:2309.16609, 2023. \bibitem{moe} Fedus et al., ``Switch Transformers,'' JMLR, 2022. \end{thebibliography} diff --git a/zen_family_overview.tex b/zen_family_overview.tex index 50a9825..e2de227 100644 --- a/zen_family_overview.tex +++ b/zen_family_overview.tex @@ -48,7 +48,7 @@ \begin{abstract} We introduce the \textbf{Zen AI Model Family}, a comprehensive suite of 10 state-of-the-art models spanning language understanding, -visual creation, design analysis, and speech recognition. Built on cutting-edge architectures from the Qwen family and optimized +visual creation, design analysis, and speech recognition. Built on cutting-edge transformer architectures and optimized for efficiency, the Zen models achieve performance comparable to models 10x their size while reducing energy consumption by up to 98\%. This whitepaper presents the complete ecosystem including 5 language models (0.6B to 480B parameters), 2 artist models for image generation and editing, 2 designer models for visual reasoning, and 1 scribe model for speech recognition. Through innovative @@ -108,14 +108,14 @@ The Zen family comprises 10 models across 4 categories: & Zen-Next & 80B & 80B & zen-72B & Flagship \\ \midrule \multirow{2}{*}{Artist} - & Zen-Artist & 8B & 8B & Qwen-Image & Generation \\ - & Zen-Artist-Edit & 7B & 7B & Qwen-Image-Edit & Editing \\ + & Zen-Artist & 8B & 8B & Zen-Image-8B & Generation \\ + & Zen-Artist-Edit & 7B & 7B & Zen-Image-Edit-7B & Editing \\ \midrule \multirow{2}{*}{Designer} - & Zen-Designer-Think & 235B & 22B & Qwen3-VL-235B-T & Reasoning \\ - & Zen-Designer-Inst & 235B & 22B & Qwen3-VL-235B & Generation \\ + & Zen-Designer-Think & 235B & 22B & Zen-VL-235B-T & Reasoning \\ + & Zen-Designer-Inst & 235B & 22B & Zen-VL-235B & Generation \\ \midrule -Scribe & Zen-Scribe & 1.5B & 1.5B & Qwen3-ASR-Flash & ASR \\ +Scribe & Zen-Scribe & 1.5B & 1.5B & Zen-ASR-1.5B & ASR \\ \bottomrule \end{tabular} \caption{Complete Zen Model Family Specifications} @@ -506,7 +506,7 @@ our planet for future generations. \section*{Acknowledgments} -We thank the open-source community, particularly the teams behind Qwen, Transformers, and GGML. Special +We thank the open-source community, particularly the teams behind Transformers and GGML. Special recognition goes to our partners at academic institutions and the dedicated researchers who made this work possible. \appendix