---

base_model: TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill

tags:

• text-generation-inference
• llama.cpp
• gguf
• unsloth
• gemma4
• reasoning

license: apache-2.0

datasets:

• TeichAI/Claude-Opus-4.6-Reasoning-887x
• TeichAI/Claude-Sonnet-4.6-Reasoning-1100x
• TeichAI/claude-4.5-opus-high-reasoning-250x
• TeichAI/Claude-Opus-4.6-Reasoning-500x
• Crownelius/Opus-4.6-Reasoning-2100x-formatted

---

🌟 Gemma 4 - 26B A4B x Claude Opus 4.6

> 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.

!Gemma 4 Benchmarks

🚀 Important Update: Version 2 Now Available

> - Upgrade Alert: A significantly enhanced version of this distillation has been released. We highly recommend switching to v2 for a superior reasoning experience and improved stability.

> - Model Link: gemma-4-26B-A4B-it-Claude-Opus-Distill-v2-GGUF

🔄 Key Enhancements in v2

We have implemented critical upgrades to further refine the model's performance:

• ✨ Dataset Quality: Re-curated high-density reasoning paths, resulting in significantly higher quality responses and more nuanced logical depth.
• 🛠️ Chat Template Fixes: Comprehensive structural fixes to the chat template to improve formatting.
• ⚖️ Generalization vs. Style: v2 was trained with a large batch size, high rank/alpha, and a low learning rate (LR). This configuration prioritizes broad generalization over specific stylistic imitation. We are currently evaluating which approach offers the best real-world utility; we are also looking for community feedback, as numbers alone don't always tell the full story.

💡 Model Introduction

Gemma 4 - 26B A4B x Claude Opus 4.6 is a highly capable model fine-tuned on top of the powerful Gemma 4 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

Base Model (unsloth/gemma-4-26B-A4B-it)
 │
 ▼
Supervised Fine-Tuning (SFT) + High-Effort Reasoning Datasets
 │
 ▼
Final Model (Gemma 4 - 26B 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:

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

Best Practices

For the best performance, use these configurations and best practices:

1. Sampling Parameters

Use the following standardized sampling configuration across all use cases:

• temperature=1.0
• top_p=0.95
• top_k=64

2. Thinking Mode Configuration

Compared to Gemma 3, the models use standard system, assistant, and user roles. To properly manage the thinking process, use the following control tokens:

• Trigger Thinking: Thinking is enabled by including the <|think|> token at the start of the system prompt. To disable thinking, remove the token.
• Standard Generation: When thinking is enabled, the model will output its internal reasoning followed by the final answer using this structure:

<|channel>thought\n[Internal reasoning]<channel|>

• Disabled Thinking Behavior: For all models except for the E2B and E4B variants, if thinking is disabled, the model will still generate the tags but with an empty thought block:

<|channel>thought\n<channel|>[Final answer]

> [!Note]

> Note that many libraries like Transformers and llama.cpp handle the complexities of the chat template for you.

3. Multi-Turn Conversations

• No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final response. Thoughts from previous model turns must not be added before the next user turn begins.

4. Modality order

• For optimal performance with multimodal inputs, place image and/or audio content before the text in your prompt.

5. Variable Image Resolution

Aside from variable aspect ratios, Gemma 4 supports variable image resolution through a configurable visual token budget, which controls how many tokens are used to represent an image. A higher token budget preserves more visual detail at the cost of additional compute, while a lower budget enables faster inference for tasks that don't require fine-grained understanding.

• The supported token budgets are: 70, 140, 280, 560, and 1120.

Use lower budgets* for classification, captioning, or video understanding, where faster inference and processing many frames outweigh fine-grained detail.

Use higher budgets* for tasks like OCR, document parsing, or reading small text.

6. Audio

Use the following prompt structures for audio processing:

• Audio Speech Recognition (ASR)

Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.

Follow these specific instructions for formatting the answer:
* Only output the transcription, with no newlines.
* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.

• Automatic Speech Translation (AST)

Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.

7. Audio and Video Length

All models support image inputs and can process videos as frames whereas the E2B and E4B models also support audio inputs. Audio supports a maximum length of 30 seconds. Video supports a maximum of 60 seconds assuming the images are processed at one frame per second.

🙏 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:

@misc{teichai_gemma4_26b_a4b_opus_distilled,
  title        = {Gemma-4-26B-A4B-it-Claude-Opus-Distill},
  author       = {TeichAI},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill}}
}