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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- graphic-design
+- design-generation
+- layout-planning
+- qwen3
+base_model: Qwen/Qwen3-8B
+---
+
+# DesignAsCode Semantic Planner
+
+The Semantic Planner for the [DesignAsCode](https://github.com/liuziyuan1109/design-as-code) pipeline. Given a natural-language design request, it generates a structured design plan — including layout reasoning, layer grouping, image generation prompts, and text element specifications.
+
+## Model Details
+
+| | |
+|---|---|
+| **Base Model** | Qwen3-8B |
+| **Fine-tuning** | Supervised Fine-Tuning (SFT) |
+| **Size** | 16 GB (fp16) |
+| **Context Window** | 8,192 tokens |
+
+## Training Data
+
+Trained on ~10k examples sampled from the [DesignAsCode Training Data](https://huggingface.co/datasets/Tony1109/DesignAsCode-training-data), which contains 19,479 design samples distilled from the [Crello](https://huggingface.co/datasets/cyberagent/crello) dataset using GPT-4o and GPT-o3. No additional data was used.
+
+### Training Format
+
+- **Input:** `prompt` — natural-language design request
+- **Output:** `layout_thought` + `grouping` + `image_generator` + `generate_text`
+
+See the [training data repo](https://huggingface.co/datasets/Tony1109/DesignAsCode-training-data) for field details.
+
+## Training Configuration
+
+| | |
+|---|---|
+| **Batch Size** | 1 |
+| **Gradient Accumulation** | 2 |
+| **Learning Rate** | 5e-5 (AdamW) |
+| **Epochs** | 2 |
+| **Max Sequence Length** | 8,192 tokens |
+| **Precision** | bfloat16 |
+| **Loss** | Completion-only (only on generated tokens) |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_path = "Tony1109/DesignAsCode-planner"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+```
+
+For full pipeline usage (plan → retrieve → implement → refine), see the [project repo](https://github.com/liuziyuan1109/design-as-code) and [QUICKSTART.md](https://github.com/liuziyuan1109/design-as-code/blob/main/QUICKSTART.md).
+
+## Outputs
+
+The model generates semi-structured text with XML tags:
+
+- `<layout_thought>...</layout_thought>` — detailed layout reasoning
+- `<grouping>...</grouping>` — JSON array grouping related layers with thematic labels
+- `<image_generator>...</image_generator>` — JSON array of per-layer image generation prompts
+- `<generate_text>...</generate_text>` — JSON array of text element specifications (font, size, alignment, etc.)
+
+## Ethical Considerations
+
+- Designs should be reviewed by humans before production use.
+- May reflect biases present in the training data.
+- Generated content should be checked for copyright compliance.
+
+## Citation
+
+```bibtex
+@article{liu2025designascode,
+  title     = {DesignAsCode: Bridging Structural Editability and 
+               Visual Fidelity in Graphic Design Generation},
+  author    = {Liu, Ziyuan and Sun, Shizhao and Huang, Danqing 
+               and Shi, Yingdong and Zhang, Meisheng and Li, Ji 
+               and Yu, Jingsong and Bian, Jiang},
+  journal   = {arXiv preprint},
+  year      = {2025}
+}
+```