How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
# Run inference directly in the terminal:
llama-cli -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
# Run inference directly in the terminal:
llama-cli -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
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 reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
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 reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
Use Docker
docker model run hf.co/reedmayhew/Grok-3-gemma3-4B-distilled:Q8_0
Quick Links

xAI Grok 3

Distilled - Gemma 3 4B

NEW REASONING VERSION AVAILABLE:

https://huggingface.co/reedmayhew/Grok-3-reasoning-gemma3-4B-distilled-GGUF

Overview

This model is a Gemma 3 4B variant distilled from xAI’s Grok 3. It was fine-tuned to emulate Grok’s depth and structured clarity, particularly in tasks involving complex thought, such as problem-solving, coding, and mathematics.

Technical Details

  • Developed by: reedmayhew
  • Base Model: google/gemma-3-4b-it
  • Training Speed Enhancement: Trained 2x faster with Unsloth and Huggingface's TRL library

Training Data

The model was trained on:

  • reedmayhew/Grok-3-100x

This dataset consists of 100 high-quality Grok 3 completions responding to deep questions, solving math problems, and writing or analyzing code. The aim was to distill Grok’s analytical approach and technical versatility into a smaller, accessible model.

This Gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.

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GGUF
Model size
4B params
Architecture
gemma3
Hardware compatibility
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8-bit

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