Ayodele01/Gemma-4-12B-Gemini-3.5-flash-Reasoning-Distill

Gemma-4-12B-Gemini-3.5-flash-Reasoning-Distill

This model is a fine-tuned version of Google's Gemma-4-12B-it (Instruction-tuned) base model, optimized via QLoRA SFT on the full 25,000 synthetic reasoning example dataset WithinUsAI/gemini_3.5_flash_distilled_25k using Unsloth.

🌟 Model Highlights

• —Task Alignment: Fine-tuned on high-quality synthetic traces distilled from Gemini 3.5 Flash, covering agentic code synthesis, dense context reasoning, mathematical engine traces, and systemic execution.
• —Structured Math JSON Engine Traces: The model has learned to represent complex mathematical reasoning as structured JSON engine traces (specifying problem_type, input_dimensions, execution_trace, and error_bounds), mimicking the mathematical_engine_traces subset (14% of the training dataset).
• —Robust Instruction Following: The fine-tuned model successfully mitigates long-tail repetition behaviors found in the base model (e.g. infinite regex loops) and guarantees clean, valid JSON schemas.
• —Stable Training: Achieved excellent training convergence (Final SFT loss: 0.2652) over 1 full epoch (6,250 steps) on a single RTX 4090.

📊 Evaluation & Capability Comparison

Both the base model and this fine-tuned model were evaluated side-by-side using identical prompts across six capability dimensions.

1. Quantitative Performance (25K SFT Run)

Generation Length & Speed by Category

2. Qualitative Discoveries & Alignment Analysis

• —Mathematical Reasoning (JSON Trace Alignment): When given a math word problem ("A fair 6-sided die is rolled 5 times..."), the fine-tuned model formats its thinking process as a structured JSON engine execution trace (specifying inputs, convolution steps, and error bounds). This mimics the mathematical_engine_traces subset (14% of training dataset).
• —Instruction Following: The base model generated invalid JSON schemas due to repetitive long-tail regex loops that hit the maximum token limit. The fine-tuned model successfully generated clean, complete, and valid JSON schemas for library book inventory, rate limiting, and pagination.
• —Debugging: Both models successfully identified and corrected critical logic bugs (infinite loops, float divisions, index bounds) in python algorithms, with the fine-tuned model showing a preference for structured code blocks.

📊 Base Model Benchmarks

According to Google DeepMind's official benchmarks for the Gemma model family (with instruction-tuned evaluations):

⚙️ Hyperparameters & Training Settings

The model was trained with the following hyperparameters:

🚀 How to Use

Loading the LoRA adapter natively with Unsloth:

import torch
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Ayodele01/Gemma-4-12B-Gemini-3.5-flash-Reasoning-Distill",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastModel.for_inference(model)

# Inference Example
messages = [{"role": "user", "content": "Write a thread-safe LRU cache with TTL in Python."}]
inputs = tokenizer(text=tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True), return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Prompt Template

This model uses the standard Gemma-4 chat format:

<|turn>user
{ prompt }<|turn>model

🔒 License & Usage

This model is subject to the Gemma Terms of Use.