gemma-4-31B-it-Claude-Opus-Distill-GGUF

Instructions to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF", dtype="auto")

llama-cpp-python

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF",
	filename="gemma-4-31B-it-Claude-Opus-Distill.bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Ollama:
```
ollama run hf.co/owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
```

Unsloth Studio

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF to start chatting

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M
```

Lemonade

How to use owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-31B-it-Claude-Opus-Distill-GGUF-Q4_K_M

List all available models

lemonade list

🌟 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.

💡 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
 │
 ▼
Final Model (Gemma 4 - 31B 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.
`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:

@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}}
}

Downloads last month: 101

GGUF

Model size

31B params

Architecture

gemma4

Hardware compatibility

3-bit

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5-bit

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Model tree for owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

unsloth/gemma-4-31B-it

Finetuned

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

Quantized

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this model

🌟 Gemma 4 - 31B x Claude Opus 4.6

💡 Model Introduction

🗺️ Training Pipeline Overview

📋 Stage Details & Benchmarks

🔹 Supervised Fine-Tuning (Meeting Claude)

📚 All Datasets Used

🌟 Core Skills & Capabilities

🙏 Acknowledgements

📖 Citation

Model tree for owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF

Datasets used to train owenqwenllmwine/gemma-4-31B-it-Claude-Opus-Distill-GGUF