# LoRA Configuration
r = 8
lora_alpha = 8
lora_dropout = 0.1

# Training Configuration
learning_rate = 5e-5
num_train_epochs = 0.5
per_device_train_batch_size = 2
gradient_accumulation_steps = 8
weight_decay = 0.01

Evaluation Results

Test Type	Score
Simple Math	3/3 (100%)
Logic Reasoning	1/1 (100%)
Complex Problems	6/8 (75%)
Overall	Matches base model

Key achievement: This fine-tuned model preserves the base model's capabilities while learning the reasoning style from training data.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill")

messages = [{"role": "user", "content": "Solve step by step: If 3x + 7 = 22, what is x?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

GGUF Versions

For llama.cpp and Ollama users, see: Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-GGUF

License

This model inherits the Gemma license.

Acknowledgments

Google for the Gemma-4 base model
Roman1111111 for the reasoning datasets
Unsloth for efficient fine-tuning