atsmt

rect2sheet-qwen-7b

README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Base model	Qwen2.5-Coder-7B-Instruct
Parameters	7.6B
Architecture	Qwen2ForCausalLM
Context length	32,768 tokens
Training data	19,231 manually verified Rect2Sheet designs
Training framework	Unsloth and Hugging Face TRL
Output	Rect2Sheet solution JSON

Usage

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 json

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "atsmt/rect2sheet-qwen-7b"
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.

llama.cpp

Quantized GGUF files are included in the model repository and can be run directly:

bash
llama-cli -hf atsmt/rect2sheet-qwen-7b --jinja

Intended Use

This model is intended for research into data-driven sheet-metal design generation and for producing candidate Rect2Sheet solutions from inputs that follow the dataset schema. It may also be useful as a baseline for constrained CAD generation and geometry-generation research.

Limitations

Generated JSON may be malformed or may not conform to the Rect2Sheet schema.
A syntactically valid output is not necessarily geometrically valid or manufacturable.
The model does not replace collision, unfolding-overlap, thin-segment, or other engineering checks.
Performance outside the tab counts, geometry, mount types, and topology represented in the training data is unknown.
No benchmark results are currently reported for this release.

Validate every generated design with geometry and manufacturability tooling, such as the SheetGen pipeline, before using it in downstream engineering or fabrication workflows.

Dataset

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. The accepted solutions were filtered for manufacturability and manually inspected. See the dataset repository for the schema, test subsets, and generation details.

Citation

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}
}

License

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.

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Model Details

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README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Base model	Qwen2.5-Coder-7B-Instruct
Parameters	7.6B
Architecture	Qwen2ForCausalLM
Context length	32,768 tokens
Training data	19,231 manually verified Rect2Sheet designs
Training framework	Unsloth and Hugging Face TRL
Output	Rect2Sheet solution JSON

Usage

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 json

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "atsmt/rect2sheet-qwen-7b"
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.

llama.cpp

Quantized GGUF files are included in the model repository and can be run directly:

bash
llama-cli -hf atsmt/rect2sheet-qwen-7b --jinja

Intended Use

Limitations

Generated JSON may be malformed or may not conform to the Rect2Sheet schema.
A syntactically valid output is not necessarily geometrically valid or manufacturable.
The model does not replace collision, unfolding-overlap, thin-segment, or other engineering checks.
Performance outside the tab counts, geometry, mount types, and topology represented in the training data is unknown.
No benchmark results are currently reported for this release.

Validate every generated design with geometry and manufacturability tooling, such as the SheetGen pipeline, before using it in downstream engineering or fabrication workflows.

Dataset

Citation

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}
}

License

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.