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README.md

@@ -1,58 +1,190 @@
 ---
-base_model: openai/gpt-oss-20b
-library_name: transformers
-model_name: gpt-oss-20b-pcb-finetune-L1
+language:
+- en
+
+license: mit
+
 tags:
-- generated_from_trainer
-- sft
-- trl
-licence: license
+- PCB
+- EDA
+- KiCAD
+- Hardware-Design
+- Schematic-Generation
+- LLM
+- Circuit-Design
+
+library_name: transformers
 ---
 
-# Model Card for gpt-oss-20b-pcb-finetune-L1
+# SchGen
 
-This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+[![License](https://img.shields.io/badge/License-MIT-green.svg)]()
+[![Model](https://img.shields.io/badge/Model-GPT--OSS--20B-blue)]()
 
-## Quick start
+**SchGen** is a large language model for **PCB schematic generation from natural-language requests**.
+
+The model is supervised fine-tuned from **GPT-OSS-20B** using a custom dataset of approximately **8K paired user requests and schematic-generation code samples**.  
+SchGen generates executable Python code that can be rendered into **KiCad schematic designs** using customized schematic APIs.
+
+➡️ **Base Model:** GPT-OSS-20B  
+➡️ **License:** MIT  
+➡️ **Framework:** Transformers  
+➡️ **Context Length:** 13,312 tokens  
+
+---
 
-```python
-from transformers import pipeline
+## Overview
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+Printed circuit board (PCB) design is a critical but expertise-intensive process in embedded systems, IoT, robotics, and AI hardware.
+
+SchGen explores whether large language models can assist hardware design by generating schematic construction code directly from natural-language descriptions.
+
+The input is a user request describing a circuit design requirement, and the output is executable Python code that can generate a KiCad schematic using custom APIs.
+
+Example input:
+
+```text
+I want a 1.8V regulated supply from VIN using an AP2112K LDO,
+with a test point on the 1.8V rail and a solder-jumper-selectable LED indicator.
 ```
 
-## Training procedure
+---
+
+## 🔥 Key Features
+
+- 🔌 **Natural Language to Schematic Code**  
+  Generates executable Python schematic-generation code directly from user requests.
+
+- 🧠 **KiCad-Oriented Design Flow**  
+  Designed around custom Code-to-Schematic APIs for KiCad schematic construction.
+
+- 📐 **Structured Hardware Generation**  
+  Produces editable and programmatic schematic representations instead of images.
+
+- 🛠️ **Research-Focused PCB Generation**  
+  Intended for experimentation, benchmarking, and AI-assisted hardware prototyping.
+
+---
+
+## Model Details
+
+| Item | Value |
+|---|---|
+| Base Model | GPT-OSS-20B |
+| Parameters | 20B |
+| Architecture | Supervised Fine-Tuned LLM |
+| Input | Natural-language design requests |
+| Output | Python schematic-generation code |
+| Context Length | 13,312 |
+| Training Hardware | 1× NVIDIA A100 |
+| Training Time | ~21 hours |
+
+---
+
+## Usage
+
+The recommended workflow is:
+
+1. Provide a natural-language circuit request
+2. Generate Python schematic-construction code
+3. Execute the code to render a KiCad schematic
+4. Verify outputs using ERC/DRC tools
+
+The model is designed for integration into:
+
+- EDA automation pipelines
+- Hardware engineering copilots
+- Synthetic schematic generation systems
+- Research workflows for AI-assisted PCB design
+
+---
+
+## Evaluation
 
- 
+SchGen was evaluated using several schematic-generation metrics:
 
+- **Valid Circuits**  
+  Measures whether generated code executes successfully and produces valid schematics.
 
-This model was trained with SFT.
+- **Spatial Violation**  
+  Measures overlaps among symbols, labels, and wires.
 
-### Framework versions
+- **Netlist Accuracy**  
+  Measures connectivity correctness against ground-truth netlists.
+
+SchGen outperforms several frontier LLM baselines on schematic generation tasks when all models are provided with the same schematic-generation APIs.
+
+---
+
+## Limitations
+
+SchGen is an early-stage research system and currently focuses on:
+
+- small and medium-scale schematic modules
+- hobbyist and open-source hardware designs
+- English-language requests
+
+The model may underperform on:
+
+- RF or high-frequency circuits
+- industrial or enterprise hardware
+- large multi-board systems
+- safety-critical applications
+
+Generated outputs should always undergo:
+
+- Electrical Rule Checking (ERC)
+- Design Rule Checking (DRC)
+- human engineering review
+
+SchGen is intended as an assistive tool rather than a fully autonomous hardware engineer.
+
+---
+
+## Technical Requirements
+
+The model generates executable Python code and requires:
+
+- Python environment
+- KiCad installation
+- Custom schematic-generation APIs
+
+Inference was validated on:
+
+- NVIDIA A100 GPUs
+- 4-bit quantized configurations
+
+---
+
+## Dataset
+
+SchGen was trained on a custom dataset of approximately 8K pairs of:
+
+- natural-language hardware requests
+- Python schematic-generation code
+
+The dataset was synthesized through:
+
+1. GPT-generated draft schematics
+2. Human correction and annotation
+3. LLM-generated user requests
+
+The dataset is available at `https://huggingface.co/datasets/microsoft/SchGen_dataset`
+
+---
+
+## License
+
+This project is licensed under the MIT License.
+
+---
 
-- TRL: 0.21.0
-- Transformers: 4.55.4
-- Pytorch: 2.8.0+cu128
-- Datasets: 4.0.0
-- Tokenizers: 0.21.4
+## Contact
 
-## Citations
+This project was conducted by members of Microsoft Research.
 
+For questions, feedback, or collaboration inquiries:
 
+- ruichunma@microsoft.com
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+If issues or problematic behavior are identified, the repository may be updated with appropriate mitigations.