Improve SVGThinker model card and add MIT license

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 SVGThinker authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -4,199 +4,136 @@ language:
 - en
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+base_model_relation: finetune
+library_name: transformers
 pipeline_tag: text-generation
+tags:
+- svg
+- text-to-svg
+- vector-graphics
+- code-generation
+- reasoning
+- qwen2
+- safetensors
+- arxiv:2509.24299
+widget:
+- text: "Generate an SVG icon of a yellow rubber duck facing right on a shallow blue oval of water."
+- text: "Generate a minimalist calendar icon with a checkmark below it."
+- text: "Generate a flat vector icon of a delivery truck using bold solid colors."
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# SVGThinker-7B
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+SVGThinker-7B is a text-to-SVG generation model introduced in
+[SVGThinker: Instruction-Aligned and Reasoning-Driven Text-to-SVG Generation](https://arxiv.org/abs/2509.24299).
+It generates editable SVG code from natural-language descriptions, with a focus on
+compact icon-style vector graphics.
 
-## Model Details
+The model is fine-tuned from
+[`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
+and is released as BF16 sharded safetensors.
 
-### Model Description
+## Links
 
-<!-- Provide a longer summary of what this model is. -->
+- Paper: [arXiv:2509.24299](https://arxiv.org/abs/2509.24299)
+- Demo Space: [Caffin/SVGThinker-7B](https://huggingface.co/spaces/Caffin/SVGThinker-7B)
+- License: MIT
 
+## Intended Use
 
+This model is intended for:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- generating SVG icons from English text prompts
+- prototyping simple vector graphics
+- producing editable SVG markup rather than raster images
+- research on text-to-SVG and structured code generation
 
-### Model Sources [optional]
+Generated SVG should be reviewed and sanitized before being rendered in production
+web pages or downstream applications.
 
-<!-- Provide the basic links for the model. -->
+## Quick Start
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```bash
+pip install "transformers>=4.51.0" torch accelerate safetensors
+```
 
-## Uses
+```python
+import re
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+model_id = "Caffin/SVGThinker-7B"
 
-### Direct Use
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    use_safetensors=True,
+)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+description = "A minimalist calendar icon with two black tabs and a bold checkmark below it."
+prompt = "Review the given information below and generate a svg according to it.\n" + description
 
-[More Information Needed]
+messages = [{"role": "user", "content": prompt}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
 
-### Downstream Use [optional]
+with torch.no_grad():
+    output = model.generate(
+        inputs,
+        max_new_tokens=4096,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.6,
+        repetition_penalty=1.05,
+    )
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+text = tokenizer.decode(output[0], skip_special_tokens=False)
+match = re.search(r"<svg.*?</svg>", text, flags=re.DOTALL)
+print(match.group(0) if match else text)
+```
 
-[More Information Needed]
+The model may emit reasoning text before the final SVG. For most applications,
+extract the `<svg>...</svg>` block before rendering.
 
-### Out-of-Scope Use
+## Model Notes
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+SVGThinker is trained directly in SVG code space. The paper describes a sequential
+annotation pipeline that aligns natural-language descriptions with the step-by-step
+construction of SVG primitives, helping the model generate more editable SVG code.
 
-[More Information Needed]
+For full training data, annotation, and evaluation details, see the paper.
 
-## Bias, Risks, and Limitations
+## Evaluation Snapshot
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+On the paper's 1,000 held-out text-to-SVG prompts, SVGThinker reports:
 
-[More Information Needed]
+| Model | FID lower is better | CLIP higher is better | FID-CLIP lower is better | Primitive support |
+| --- | ---: | ---: | ---: | --- |
+| SVGThinker-7B | 34.06 | 0.2765 | 21.08 | all |
 
-### Recommendations
+## Limitations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Outputs may be malformed, incomplete, or visually inconsistent with the prompt.
+- The model is best suited for simple to moderately complex icon-style graphics.
+- It may struggle with photorealistic scenes, dense layouts, and text-heavy SVGs.
+- SVG is executable markup in browser contexts; treat generated SVG as untrusted.
+- The model primarily targets English prompts.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+## Citation
+
+```bibtex
+@inproceedings{chen2025svgthinker,
+  title = {SVGThinker: Instruction-Aligned and Reasoning-Driven Text-to-SVG Generation},
+  author = {Chen, Hanqi and Zhao, Zhongyin and Chen, Ye and Liang, Zhujin and Ni, Bingbing},
+  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
+  year = {2025},
+  publisher = {ACM},
+  doi = {10.1145/3746027.3755392},
+  url = {https://arxiv.org/abs/2509.24299}
+}
+```