sadl_qwen2.5_3b

Model Details

Model Description

• Developed by: chambersc2017
• Model type: LoRA adapter (PEFT) on Qwen2.5-3B-Instruct
• Language(s): English
• License: See repository for license details
• Fine-tuned from: Qwen/Qwen2.5-3B-Instruct
• Framework: PEFT 0.18.1 / Transformers

Model Sources

• Base model: Qwen/Qwen2.5-3B-Instruct
• Project repository: https://github.com/CandiceChambers1/SysGenAI.git

---

Uses

Direct Use

This model is intended to be used as part of the SysGenAI pipeline. Given a natural language description of a system, the model generates corresponding SADL translation, which can then be rendered as into architecture diagrams (AADL/SysML).

Demonstration:

• English natural language: "Inside the coffeemaker, two components reside: a cooking unit and a storage unit."

• SADL: "COFFEEMAKER consists internal_components : COOKING_UNIT and STORAGE_UNIT."

• English natural language: "The power unit houses five internal components: a cord, a power switch, a junction box, heating wires, and motor wires."

• SADL: "POWER_UNIT consists internal_components : CORD , POWER_SWITCH , JUNCTION_BOX , HEATING_WIRES and MOTOR_WIRES."

• English natural language: "A filter holder and a filter insert are recognized as the internal components of the HEPA assembly."

• SADL: "HEPA_ASSEMBLY consists internal_components : FILTER_HOLDER and FILTER_INSERT."

Example use cases:

• Generating AADL component definitions from a system narrative
• Producing SysML block diagrams from requirements text
• Rapid prototyping of system architecture models without manual modeling effort

Downstream Use

The adapter is designed to slot into the SysGenAI project pipeline:

1. User provides a plain-English system description as input
2. This model generates formal SADL translation
3. Output is passed to SDD_Generator.java for diagram rendering
4. Final diagrams are written to src/gen/aadl/ or src/gen/sysml/

Out-of-Scope Use

• Not intended for general-purpose text generation or chat
• Not validated for safety-critical or production-grade architecture modeling without human review
• Not suitable for non-English input descriptions
• Not designed for large or highly complex system architectures

---

How to Get Started with the Model

Install dependencies:

bash
Copy code
pip install transformers peft torch

Load the adapter:

python
Copy code
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "Qwen/Qwen2.5-3B-Instruct"
adapter_id = "chambersc2017/sadl_qwen2.5_3b"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Describe a simple sensor system with a data processor and output interface."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Note: This adapter is used within the SysGenAI project pipeline. For full diagram generation, run it alongside SDD_Generator.java as described in the project README.

---

Training Details

Training Data

The model was fine-tuned on a 50,000-pair synthetically generated dataset created specifically for the SysGenAI project. The dataset consists of paired examples of:

• Natural language descriptions of the system
• Corresponding SADL representations

The synthetic data was designed to cover a range of system architectures, including component hierarchies, data flows, and interface definitions.

Training Procedure

• Method: LoRA (Low-Rank Adaptation) via PEFT
• Base model: Qwen2.5-3B-Instruct
• Hardware: NVIDIA GeForce RTX 5090 (32GB VRAM)
• Driver Version: 580.126.09
• CUDA Version: 13.0
• Framework: PEFT 0.18.1

Training Hyperparameters

---

Evaluation

Testing Data

Evaluation was performed on a held-out split of the synthetic dataset, covering system descriptions not seen during training.

Metrics

• BLEU score
  • BLEU (Bilingual Evaluation Understudy) is a precision-focused text generation metric that evaluates machine-generated translations by measuring n-gram overlap against one or more human reference translations.
• Accuracy
  • Accuracy is the proportion of predictions that exactly match the reference.
• Parsability
  • Parsability refers to the degree to which a machine translation adheres to the syntactic and structural constraints defined by the SADL grammar, ensuring that the output can be deterministically parsed and interpreted by systems that implement SADL.

Results

Evaluated on 300 handwritten natural language descriptions:

---

Bias, Risks, and Limitations

Known Limitations

• English only: The model has only been tested on English-language system descriptions. Non-English input is not supported and may produce unpredictable output.
• Simple to moderate systems only: The model performs best on smaller, well-scoped system descriptions. Complex, highly nested, or large-scale architectures may result in incomplete or malformed output.
• May hallucinate invalid syntax: As with all language models, this adapter can generate SADL translations that appears structurally plausible but contains syntax errors or semantically incorrect constructs. All output should be reviewed by a domain expert before use.

Recommendations

• Always validate generated SADL output using a domain-appropriate parser or linter before integrating into a formal design
• Use this model as a drafting aid, not a replacement for expert architecture modeling
• Keep input descriptions concise and focused on a single system or subsystem for best results

---

Citation

If you use this model in academic work, please cite the base model and this adapter:

bibtex
Copy code
@misc{sadl_qwen2025,
  author       = {chambersc2017},
  title        = {SysGenAI: Qwen2.5-3B LoRA Adapter for NL-to-SADL Translation},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/chambersc2017/sadl_qwen2.5_3b}}
}

---

Model Card Contact

For questions or issues, open a discussion on this repository.

---

Framework Versions

• PEFT 0.18.1
• Transformers 4.57.3
• PyTorch 2.9.0