Circuit Reasoning Google Gemma 4 31B It Thinking Enabled

--- language: - en tags: - vision - image-to-text - unsloth - hardware - iot - esp32 - circuit-reasoning - chain-of-thought base_model: google/gemma-4-31b-it --- # Circuit Reasoning VLM (Gemma Fine-tuned) **Model Used:** [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) | **Blog Post:** [BLOG-NAME](https://www.leadingtorch.com/2026/02/09/maximizing-accuracy-with-vlms-replacing-ocr-pipelines-with-pydantic-structured-outputs/) | **Authors:** [Leading Torch Pvt. Ltd.](https://www.leadingtorch.com/) ## Introduction Large Vision-Language Models (VLMs) have shown incredible progress in understanding natural images, but they still struggle with complex spatial reasoning in technical domains. If you show a typical VLM a picture of an ESP32 wired to a sensor on a breadboard, it might tell you "there are wires and a chip," but it can rarely trace the exact connections—like telling you that the green wire is plugged into D18 instead of D19. For electronics students, hobbyists, and QA engineers, catching these tiny wiring mistakes is critical, yet often tedious. That’s why we’ve trained a specialized Circuit Reasoning model. This model uses "Chain of Thought" (CoT) reasoning to act as a virtual hardware assistant, systematically checking your power, ground, and data lines against a schematic and a textual pinout reference before you ever plug in the power. ## 📌 Primary Goal The primary goals of this model are to: - Improve structured reasoning ability for visual circuit inspection. - Provide step-by-step Chain-of-Thought (CoT) explanations for hardware validation. - Detect wiring errors in circuits. ## 📊 Training Data The model was fine-tuned on a custom, high-quality circuit wiring dataset. This dataset consists of paired inputs: a textual ESP32 pinout reference, a reference schematic image, and a corresponding breadboard circuit photo (which may be correctly or incorrectly wired). It features step-by-step reasoning traces and final conclusions designed to teach the model how to audit physical builds against reference diagrams and pin definitions. ## 🗺️ Training Pipeline Overview ``` Base Model (google/gemma-4-31b-it) │ ▼ gemma-4-31b-it with Unsloth optimization │ ▼ Supervised Fine-Tuning (SFT) + LoRA (Using custom circuit-reasoning data with Chain-of-Thought) │ ▼ leadingtorch/circuit-reasoning-google-gemma-4-31B-it-thinking-en...