--- license: apache-2.0 base_model: Qwen/Qwen3-4B-Instruct-2507 datasets: - gcelikmasat-work/BPMN-IT-Dataset language: - en library_name: transformers pipeline_tag: text-generation tags: - bpmn - business-process-modeling - process-modeling - instruction-tuning - lora - peft - dot - graphviz - llama-factory - qwen3 model-index: - name: Qwen3-4B-InstruBPM results: - task: type: text-generation name: BPMN Model Generation from Text dataset: type: gcelikmasat-work/BPMN-IT-Dataset name: BPMN-IT (stratified 180-instance benchmark across 15 business domains, seed split) metrics: - type: bleu value: 83.06 name: BLEU - type: rouge value: 94.43 name: ROUGE-L - type: meteor value: 92.82 name: METEOR - type: relative-graph-edit-distance value: 99.44 name: R-GED Accuracy (%) --- # Qwen3-4B-InstruBPM Qwen3-4B-InstruBPM is a compact, instruction-tuned language model that converts natural-language business process descriptions into BPMN models rendered in [Graphviz DOT](https://graphviz.org/doc/info/lang.html). It is a LoRA adaptation of [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), trained on a cleaned, stratified subset of the [MaD dataset](https://ieeexplore.ieee.org/abstract/document/10191898) for the paper: > **InstruBPM: Instruction-Tuning Open-Weight Language Models for BPMN Model Generation.** > Gökberk Çelikmasat, Atay Özgövde, Fatma Başak Aydemir. *Software and Systems Modeling*, under review, 2026. > arXiv: [2512.12063](https://arxiv.org/abs/2512.12063) On a 180-instance benchmark stratified by difficulty across 15 business domains, this model attains near-perfect structural fidelity (R-GED Accuracy ≈ 99.4%) and matches or outperforms both untuned open-weight baselines (Qwen2.5 7/14B, Qwen3 30B, Qwen3-Coder) and strong proprietary systems (GPT-5.1, Claude 4.5 Sonnet/Haiku, Gemini 2.5 Pro/Flash) on BLEU, ROUGE-L, and METEOR — at roughly half the parameter count of our prior tuned model. ## Results Evaluation on the 180-instance stratified benchmark (paper Table 2). Higher is better on all four metrics. | Model | BLEU | ROUGE-L | METEOR | R-GED Acc. | | -------------------------------------- | ----: | ------: | -----: | ---------: | | **Qwen3-4B-InstruBPM** (this model) | **83.06** | **94.43** | **92.82** | **99.44** | | [Gemma2-9B-BPMG-IT](https://huggingface.co/gcelikmasat-work/gemma-2-9b-it-BPMN) (prior work) | 82.98 | 94.61 | 92.67 | 97.78 | | Qwen3-Coder-30B-A3B-Instruct | 8.06 | 43.00 | 45.07 | 38.21 | | Qwen3-30B-A3B-Instruct-2507 | 6.66 | 42.28 | 44.79 | 38.68 | | Qwen3-4B-Instruct-2507 (base) | 2.89 | 40.31 | 44.16 | 44.47 | | Gemini 2.5 Pro | 28.72 | 48.98 | 63.66 | 43.58 | | Claude 4.5 Sonnet | 22.56 | 49.87 | 61.37 | 41.47 | | Claude 4.5 Haiku | 18.15 | 46.69 | 58.21 | 35.91 | | Gemini 2.5 Flash | 15.24 | 47.18 | 57.69 | 30.07 | | GPT-5.1 | 12.64 | 48.83 | 59.01 | 40.95 | Per-domain R-GED Accuracy is 100% in 14 of 15 domains (paper Table 3). Friedman tests with Kendall's *W* between 0.65 and 0.81 and bootstrap confidence intervals confirm these differences are statistically significant (paper Appendix A). ## Intended use Generate first-draft BPMN models from textual process descriptions to accelerate early-stage modeling. In expert review, the outputs were judged to be usable with modest post-editing and to follow BPMN best practices for model size, explicit gateways, split/join consistency, and process orientation (paper §6.2, BEBoP verification). The model is intended as an **assistant for business process modelers and analysts**, not as a fully autonomous replacement for manual modeling. Human review is recommended, particularly for gateway logic and activity labels in ambiguous descriptions. ## Supported BPMN subset The model generates BPMN process fragments in DOT notation covering: **start events, end events, tasks (activities), sequence flows, and AND/XOR gateways (splits and joins).** It does **not** currently generate pools, lanes, message flows, data objects, intermediate/boundary events, sub-processes, or annotations. ## How to use ### With `transformers` ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "gcelikmasat-work/Qwen3_4B_BPMN_IT" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) instruction = ( "You are an expert in BPMN modeling and DOT language. Your task is to " "convert detailed textual descriptions of business processes into accurate " "BPMN model codes written in DOT language. Label all nodes with their " "activity names. Represent all connections between nodes without labeling " "the connections. Represent each node and its connections accurately, " "ensuring all decision points and flows are included and connected. " "Now, generate BPMN business process model code in DOT language for the " "following textual description of a business process: " ) description = ( "The process begins when the customer submits an application. After submission, " "the application is reviewed by the credit officer. If the application is approved, " "the loan is disbursed. Otherwise, a rejection letter is sent. The process ends." ) messages = [{"role": "user", "content": instruction + description}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([prompt], return_tensors="pt").to(model.device) with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=2048, temperature=0.1, top_p=1.0, do_sample=True) dot_code = tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True) print(dot_code) ``` ### With `vLLM` (recommended for batched inference) ```python from vllm import LLM, SamplingParams llm = LLM(model="gcelikmasat-work/Qwen3_4B_BPMN_IT", max_model_len=2048) params = SamplingParams(temperature=0.1, top_p=1.0, max_tokens=2048) outputs = llm.chat([[{"role": "user", "content": instruction + description}]], params) print(outputs[0].outputs[0].text) ``` The generated DOT can be rendered with Graphviz: ```bash dot -Tpng process.dot -o process.png ``` ## Training | Parameter | Value | | ---------------------------- | ------------------------------- | | Base model | Qwen/Qwen3-4B-Instruct-2507 | | Training framework | LLaMA-Factory | | Adapter | LoRA, `all` target modules | | LoRA rank `r` | 16 | | LoRA α | 32 | | LoRA dropout | 0.05 | | Precision | bf16 | | Cutoff length | 2048 tokens | | Batch size (per device) | 16 | | Gradient accumulation steps | 2 | | Epochs | 1 (≈670 optimizer steps) | | Learning rate | 2 × 10⁻⁴ | | LR schedule / warmup ratio | cosine / 0.05 | | Optimiser | AdamW (torch) | | FlashAttention / Liger | FA2 / enabled | | Hardware | 2 × NVIDIA L40S (48 GB) | | Wall-clock | ≈150 minutes | | Decoding at inference | temperature=0.1, top_p=1.0, max_tokens=2048 | **Training data.** 21.5k cleaned instruction–input–output triples from the MaD dataset, split 80/10/10 for train/validation/test. Filtering removed malformed DOT, duplicate processes, disconnected components, and descriptions exceeding 2048 tokens. The full splits are available at [`gcelikmasat-work/BPMN-IT-Dataset`](https://huggingface.co/datasets/gcelikmasat-work/BPMN-IT-Dataset). ## Deployment variants This repository hosts the merged BF16 checkpoint. Two related collections provide variants for deployment trade-offs discussed in the paper: - **GGUF quantizations (paper Table 5)** — Q2 through Q8 via HQQ/llama.cpp. Mid-precision (Q5–Q8) preserves near-BF16 quality with roughly half the memory footprint: [Qwen3-4b-Different-Quantization-GGUF](https://huggingface.co/collections/gcelikmasat-work/qwen3-4b-different-quantization-gguf). - **Merge-time α variants (paper Table 6)** — α ∈ {8, 16, 32, 64} applied during LoRA merge, holding rank at 16. Mid-range α (16–32) gives the best accuracy; α=32 is the default in this checkpoint: [Qwen3-4b-Different-Alpha](https://huggingface.co/collections/gcelikmasat-work/qwen3-4b-different-alpha). ## Limitations - **Scope.** Generates the control-flow slice of BPMN (tasks, events, sequence flows, AND/XOR gateways). Does not yet handle pools, lanes, message flows, data objects, or sub-processes. - **Language.** Trained on English only. - **Domain shift.** Evaluated on a stratified 180-instance held-out benchmark from the MaD dataset. Generalization to enterprise documentation with different terminology or structure is not fully established. - **Label quality.** Expert reviewers occasionally observed overly generic activity labels when input descriptions were vague, and BEBoP verification found gaps in default-flow and XOR-label coverage (paper §6.2, Table 8). - **Semantic equivalence.** High structural similarity (R-GED) does not guarantee semantic equivalence — two structurally identical graphs can differ in intent when descriptions are underspecified. ## Citation ```bibtex @article{celikmasat2026instrubpm, title = {InstruBPM: Instruction-Tuning Open-Weight Language Models for BPMN Model Generation}, author = {{\c{C}}elikmasat, G{\"o}kberk and {\"O}zg{\"o}vde, Atay and Aydemir, Fatma Ba{\c{s}}ak}, journal = {Software and Systems Modeling}, year = {2026}, note = {Under review. arXiv:2512.12063}, url = {https://arxiv.org/abs/2512.12063} } ``` Please also cite the source dataset: ```bibtex @inproceedings{li2023mad, title = {{MaD}: A Dataset for Interview-based {BPM} in Business Process Management}, author = {Li, Xiang and Ni, Lijuan and Li, Ran and Liu, Jiafei and Zhang, Ming}, booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)}, pages = {1--8}, year = {2023}, publisher = {IEEE} } ``` ## License Apache 2.0, inherited from the base model ([`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)). The training data is distributed separately under the terms of the MaD dataset. ## Acknowledgements This work builds on our prior instruction-tuning effort on Gemma2-9B ([Çelikmasat et al., PROFES 2025](https://doi.org/10.1007/978-3-032-12089-2_17)), available at [`gcelikmasat-work/gemma-2-9b-it-BPMN`](https://huggingface.co/gcelikmasat-work/gemma-2-9b-it-BPMN). We thank the authors of the [MaD dataset](https://ieeexplore.ieee.org/abstract/document/10191898) for making their resource publicly available.