🌟 Gemma 4 - 31B 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-4-31B-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 - 31B 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

Base Model (unsloth/gemma-4-31B-it)
 │
 ▼
Supervised Fine-Tuning (SFT) + High-Effort Reasoning Datasets
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Final Model (Gemma 4 - 31B x Claude Opus 4.6)

📋 Stage Details & Benchmarks

👁 Benchmark Comparison

TeichAI/gemma-4-31B-it-Claude-Opus-Distill
 arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.540,0.708,0.891,0.733,0.434,0.788,0.686

gemma-4-31B-it
 arc arc/e boolq hswag obkqa piqa wino
qx86-hi 0.496,0.653,0.901,0.624,0.380,0.732,0.653

Provided by @nightmedia, big thanks for taking the time

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:

🌟 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.

Getting Started

You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:

pip install -U transformers torch accelerate

Once you have everything installed, you can proceed to load the model with the code below:

from transformers import AutoProcessor, AutoModelForCausalLM

MODEL_ID = "google/gemma-4-31B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
 MODEL_ID,
 dtype="auto",
 device_map="auto"
)

Once the model is loaded, you can start generating output:

# Prompt
messages = [
 {"role": "system", "content": "You are a helpful assistant."},
 {"role": "user", "content": "Write a short joke about saving RAM."},
]

# Process input
text = processor.apply_chat_template(
 messages, 
 tokenize=False, 
 add_generation_prompt=True, 
 enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
processor.parse_response(response)

To enable reasoning, set enable_thinking=True and the parse_response function will take care of parsing the thinking output.

Below, you will also find snippets for processing audio (E2B and E4B only), images, and video alongside text:

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 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_31b_opus_distilled,
 title = {Gemma-4-31B-it-Claude-Opus-Distill},
 author = {TeichAI},
 year = {2026},
 publisher = {Hugging Face},
 howpublished = {\url{https://huggingface.co/TeichAI/gemma-4-31B-it-Claude-Opus-Distill}}
}

Base model

google/gemma-4-31B

google/gemma-4-31B-it

unsloth/gemma-4-31B-it

TeichAI/gemma-4-31B-it-Claude-Opus-Distill

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