Build Environment & Features:

Fine-tuning Framework: Unsloth

Reasoning Effort: High

This model bridges the gap between Google's exceptional open-weights architecture and Claude 4.6's profound reasoning capabilities, leveraging cutting-edge fine-tuning environments.

💡 Model Introduction

Gemma 4 - 27B A4B x Claude Opus 4.6 is a highly capable model fine-tuned on top of the powerful unsloth/gemma-4-31B-it architecture. The model's core directive is to absorb state-of-the-art reasoning distillation, primarily sourced from Claude-4.6 Opus interactions.

By utilizing datasets where the reasoning effort was explicitly set to High, this model excels in breaking down complex problems and delivering precise, nuanced solutions across a variety of demanding domains.

🗺️ Training Pipeline Overview

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Base Model (unsloth/gemma-4-27B-A4B-it)
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Supervised Fine-Tuning (SFT) + High-Effort Reasoning Datasets
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Final Model (Gemma 4 - 27B A4B x Claude Opus 4.6)

📋 Stage Details & Benchmarks

Performance vs Size:

Deep Dive Analysis: For more comprehensive insights regarding the base capabilities of the Gemma 4 architecture, please refer to this Analysis Document.

🔹 Supervised Fine-Tuning (Meeting Claude)

Objective: To inject high-density reasoning logic and establish a strict format for complex problem-solving.
Methodology: We utilized Unsloth for highly efficient memory and compute optimization during the fine-tuning process. The model was trained extensively on various reasoning trajectories from Claude Opus 4.6 to adopt a structured and efficient thinking pattern.

📚 All Datasets Used

The dataset consists of high-quality, high-effort reasoning distillation data:

Dataset Name	Description / Purpose
`TeichAI/Claude-Opus-4.6-Reasoning-887x`	Core Claude 4.6 Opus reasoning trajectories.
`TeichAI/Claude-Sonnet-4.6-Reasoning-1100x`	Additional high-density reasoning instances from Claude 4.6 Sonnet.
`TeichAI/claude-4.5-opus-high-reasoning-250x`	Legacy high-intensity reasoning distillation.
`TeichAI/Claude-Opus-4.6-Reasoning-500x`	Additional Opus 4.6 reasoning traces targeting domain diversity
`Crownelius/Opus-4.6-Reasoning-2100x-formatted`	Crownelius's extensively formatted Opus reasoning dataset for structural reinforcement.

🌟 Core Skills & Capabilities

Thanks to its robust base model and high-effort reasoning distillation, this model is highly optimized for the following use cases:

💻 Coding: Advanced code generation, debugging, and software architecture planning.
🔬 Science: Deep scientific reasoning, hypothesis evaluation, and analytical problem-solving.
🔎 Deep Research: Navigating complex, multi-step research queries and synthesizing vast amounts of information.
🧠 General Purpose: Highly capable instruction-following for everyday tasks requiring high logical coherence.

🙏 Acknowledgements

Google: For providing an exceptional open weights model. Read more about Gemma 4 on the Google Innovation Blog.
Unsloth: For assembling ready-to-use, cutting-edge fine-tuning environments that make this work possible.
Crownelius: For creating and sharing his awesome Opus reasoning dataset with the community.

📖 Citation

If you use this model in your research or projects, please cite:

bibtex

@misc{teichai_gemma4_27b_a4b_opus_distilled,
  title        = {Gemma-4-27B-A4B-it-Claude-Opus-Distill},
  author       = {TeichAI},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/TeichAI/gemma-4-27B-A4B-it-Claude-Opus-Distill}}
}

beaupi/gemma-4-26B-A4B-it-Claude-Opus-Distill-oQ6

🌟 Gemma 4 - 27B A4B x Claude Opus 4.6