Gemma-4 E4B Gemini 3.1 Pro Reasoning Distill - GGUF

GGUF quantized versions of Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill.

Model Description

This is Google's Gemma-4 4B (E4B) instruction-tuned model, fine-tuned on Gemini 3.1 Pro reasoning datasets to improve chain-of-thought reasoning capabilities.

Training Data

Training Configuration

  • Base Model: google/gemma-4b-it (via unsloth/gemma-4-E4B-it)
  • Method: LoRA fine-tuning
  • LoRA Config: r=8, alpha=8, dropout=0.1
  • Learning Rate: 5e-5
  • Epochs: 0.5
  • Framework: Unsloth + TRL

Available Quantizations

Filename Quant Type Size BPW Description
gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-F16.gguf F16 15GB 16.0 Full precision, best quality
gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q8_0.gguf Q8_0 8GB 8.5 High quality, good for most use cases
gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q5_K_M.gguf Q5_K_M 5.8GB 6.1 Balanced quality/size, recommended
gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q4_K_M.gguf Q4_K_M 5.3GB 5.0 Good quality, smaller size
gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q3_K_M.gguf Q3_K_M 4.6GB 5.1 Smallest, for constrained devices

Usage with llama.cpp

# Download a quantized model
wget https://huggingface.co/Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-GGUF/resolve/main/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q8_0.gguf

# Run with llama.cpp
./llama-cli -m gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q8_0.gguf \
  -p "What is the sum of all prime numbers between 1 and 50?" \
  -n 512

Usage with Ollama

Create a Modelfile:

FROM ./gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-Q8_0.gguf

TEMPLATE """<bos><start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model
"""

PARAMETER stop "<end_of_turn>"
PARAMETER temperature 0.7

Then:

ollama create gemma4-reasoning -f Modelfile
ollama run gemma4-reasoning

Recommended Settings

  • Temperature: 0.7
  • Top-P: 0.9
  • Context Length: 8192 (model supports up to 128K)
  • Repeat Penalty: 1.1

License

This model is released under the Gemma License.

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