A locally fine-tuned Gemma 4 E4B model optimized for structured reasoning, multi-step problem solving, and efficient inference on Apple Silicon and GGUF-compatible runtimes.
Overview
This model was fine-tuned using LoRA on a curated dataset of approximately 11k reasoning-focused examples, merged from two high-quality sources. The result is a compact model that preserves Gemma's conversational fluency while improving logical coherence and response structure.
Property
Value
Base model
mlx-community/gemma-4-e4b-it-4bit
Fine-tuning
LoRA (rank=16, layers=8)
Training steps
15k iterations
Training samples
~11k conversations
Export format
GGUF (q8_0)
Target runtime
Apple Silicon M-series, llama.cpp, Ollama, mlx-lm
Dataset
Training data was built by merging and normalizing two sources:
Note: Ollama's Modelfile format does not support the full Jinja template. For tool calling, use llama-server with --chat-template-file.
Notes
Tool calling is experimental. The model was not fine-tuned on tool-use data. Tool calling works through prompt injection via the chat template, relying on the base Gemma 4 model's pre-training. Simple single-tool calls work; complex multi-tool chains may be inconsistent.
The base model was quantized from a 4-bit MLX checkpoint, not from full-precision weights — some quality loss was already present before GGUF conversion.
Internal reasoning tokens (think blocks) are removed via training data preprocessing, not post-processing.