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README.md

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+---
+license: apache-2.0
+tags:
+- code-generation
+- plc
+- iec-61131-3
+- structured-text
+- gemma
+- phi
+- industrial-automation
+---
+
+# PLC Code Generation Models (IEC 61131-3)
+
+This repository contains fine-tuned checkpoints for generating Programmable Logic Controller (PLC) code in **Structured Text (IEC 61131-3)** format. These models have been explicitly trained and benchmarked for air-gapped, edge-hardware deployments in industrial settings.
+
+## Models Included
+
+This repository hosts weights for two distinct model families:
+
+### 1. Gemma 3 (4B) - `Gemma3-PLC-4B/`
+A fine-tuned version of Google's Gemma 3 (4B parameter) model. It was fine-tuned for high-accuracy complex logic generation and benchmarked for latency and throughput on edge GPUs (e.g., NVIDIA A4000). 
+- **Folders Included**: `model_merged`, `model_merged_hacked`, and intermediate `checkpoints_*`.
+
+### 2. Phi - `Phi-PLC-Lora/`
+A highly efficient fine-tune of Microsoft's Phi model. Provided as both merged weights and standalone LoRA adapters.
+- **Folders Included**: `model_phi_merged`, `model_phi_lora`.
+
+## Intended Use
+
+- **Task**: Natural Language to PLC Structured Text (IEC 61131-3).
+- **Environment**: Optimized for local, on-premise edge inference where internet access is restricted (air-gapped factory environments).
+- **Users**: Control Systems Engineers, Automation Programmers, and Robotics Researchers.
+
+## Training Data
+The models were trained on a highly curated dataset consisting of:
+- Standardized open-source PLC libraries (e.g., OSCAT).
+- Custom verified industrial control logic datasets.
+
+## Benchmarking & Performance
+These models were evaluated against zero-shot baselines for both generation accuracy and inference speed. They achieve state-of-the-art performance for localized IEC 61131-3 generation, significantly outperforming base models on complex automation tasks. Detailed benchmarking scripts and data parsing logic can be found in the associated GitHub repository.