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atsmt
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Model Details
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atsmt
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Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base model | Qwen2.5-Coder-7B-Instruct |
| Parameters | 7.6B |
| Architecture | Qwen2ForCausalLM |
| Context length | 32,768 tokens |
| Training data | 80,028 unvalidated Rect2Sheet designs |
| Training framework | Unsloth and Hugging Face TRL |
| Output | Rect2Sheet solution JSON |
The prompt should contain one complete Rect2Sheet rectangle JSON object. Explicitly request only the solution JSON so the response can be parsed directly.
python
import jsonimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "atsmt/rect2sheet-qwen-7b-80k"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype="auto",device_map="auto",)with open("001_rectangle.json", encoding="utf-8") as file:rectangle = json.load(file)messages = [{"role": "user","content": ("Generate a Rect2Sheet sheet-metal solution for the following rectangle layout. ""Return only valid solution JSON.\n\n"+ json.dumps(rectangle)),}]prompt = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True,)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.inference_mode():output = model.generate(**inputs, max_new_tokens=4096, do_sample=False)generated = output[0, inputs.input_ids.shape[1]:]print(tokenizer.decode(generated, skip_special_tokens=True))
Example rectangle and solution files are available in the repository's
dataset_test_1/dataset_json
directory.
A quantized GGUF file is included in the model repository and can be run directly:
bash
llama-cli -hf atsmt/rect2sheet-qwen-7b-80k --jinja
This model is intended for research into data-driven sheet-metal design generation, especially comparisons between larger unvalidated synthetic datasets and smaller manually verified datasets. It can produce candidate Rect2Sheet solutions from inputs that follow the dataset schema.
Validate every generated design with geometry and manufacturability tooling, such as the SheetGen pipeline, before
using it in downstream engineering or fabrication workflows. Prefer the manually verified
rect2sheet-qwen-7b variant when training-data quality is more important
than dataset size.
Rect2Sheet pairs rectangle layouts with sheet-metal solutions. Inputs describe tabs through corner points A, B,
and C, with optional mounts. Targets describe the fold sequence, bends with tab and point references, bend
direction, and the resulting tab geometry. See the dataset repository for the
schema, test subsets, and generation details.
Please cite the Rect2Sheet dataset and SheetGen when using this model:
bibtex
@dataset{tender2026rect2sheet,author = {Tender, A. M. and Wittig Adão, C. and Matthiesen, S.},title = {Rect2Sheet: A Dataset of Sheet Metal Connection Designs},year = {2026},publisher = {Karlsruhe Institute of Technology},doi = {10.5281/zenodo.20834240},url = {https://doi.org/10.5281/zenodo.20834240}}
This model is released under the Apache License 2.0. The Rect2Sheet dataset and upstream model may have their own terms; review them before use.