Improve model card: Add pipeline tag, library name, and sample usage

README.md

@@ -1,15 +1,14 @@
 ---
-license: apache-2.0
 language:
 - en
 - ko
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+base_model: Qwen/Qwen3-VL-32B
 ---
 
-
-
-
-
-#  gWorld-32B 🌍📱 
+# gWorld-32B 🌍📱 
 
 <p align="center">
 <picture>
@@ -51,7 +50,7 @@ language:
 </p>
 
 **gWorld-32B** establishes a new **Pareto frontier** in the trade-off between model size and GUI world modeling accuracy. 
-- **Efficiency:** Outperforms frontier models up to **12.6x larger** (e.g., `Llama 4 402B0-A17B`) on GUI-specific benchmarks.
+- **Efficiency:** Outperforms frontier models up to **12.6x larger** (e.g., `Llama 4 402B-A17B`) on GUI-specific benchmarks.
 - **Accuracy:** Achieves a **+27.1% gain** in Instruction Accuracy (IAcc.) over the base Qwen3-VL model.
 - **Zero-Shot Generalization:** Demonstrated high performance on out-of-distribution benchmarks like AndroidWorld and KApps (Korean).
 
@@ -65,21 +64,125 @@ The model treats the mobile interface as a coordinate space and predicts how tha
 By outputting HTML/CSS, gWorld ensures that text remains perfectly sharp and layouts are responsive.
 - **High Renderability:** <1% render failure rate.
 - **Speed:** Rendering via Playwright takes ~0.3s, significantly faster than multi-step diffusion pipelines.
-- **Setup:** For rendering utilities, visit the [official GitHub](https://github.com/trillion-labs/gWorld).
+
+## Sample Usage
+
+### Inference with vLLM
+
+To use the model, you can use the following snippet from the official repository:
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoProcessor
+from PIL import Image
+
+# Model configuration
+MODEL_PATH = "trillionlabs/gWorld-32B"
+BASE_MODEL = "Qwen/Qwen3-VL-32B"
+
+# Image processing settings
+MM_PROCESSOR_KWARGS = {
+    "max_pixels": 4233600,
+    "min_pixels": 3136,
+}
+
+# Load model
+llm = LLM(
+    model=MODEL_PATH,
+    tokenizer=BASE_MODEL,
+    tensor_parallel_size=8,
+    gpu_memory_utilization=0.9,
+    max_model_len=19384,
+    trust_remote_code=True,
+    mm_processor_kwargs=MM_PROCESSOR_KWARGS,
+    enable_chunked_prefill=True,
+    max_num_batched_tokens=16384,
+)
+
+# Load processor for chat template
+processor = AutoProcessor.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+# Prepare input
+image = Image.open("screenshot.png") # Replace with your screenshot
+if image.mode != 'RGB':
+    image = image.convert('RGB')
+
+action = '{"action_type": "TAP", "coordinates": [512, 890]}'
+
+# World model prompt template
+user_content = f"""You are an expert mobile UI World Model that can accurately predict the next state given an action.
+Given a screenshot of a mobile interface and an action, you must generate clean, responsive HTML code that represents the state of the interface AFTER the action is performed.
+First generate reasoning about what the next state should look like based on the action.
+Afterwards, generate the HTML code representing the next state that logically follows the action.
+You will render this HTML in a mobile viewport to see how similar it looks and acts like the mobile screenshot.
+
+Requirements:
+1. Provide reasoning about what the next state should look like based on the action
+2. Generate complete, valid HTML5 code
+3. Choose between using inline CSS and utility classes from Bootstrap, Tailwind CSS, or MUI for styling, depending on which option generates the closest code to the screenshot.
+4. Use mobile-first design principles matching screenshot dimensions.
+5. For images, use inline SVG placeholders with explicit width and height attributes that match the approximate dimensions from the screenshot. Matching the approximate color is also good.
+6. Use modern web standards and best practices
+7. Return ONLY the HTML code, no explanations or markdown formatting
+8. The generated HTML should render properly in a mobile viewport.
+9. Generated HTML should look like the screen that logically follows the current screen and the action.
+
+Action:
+{action}
+
+Output format:
+# Next State Reasoning: <your reasoning about what the next state should look like>
+# HTML: <valid_html_code>
+
+Generate the next state reasoning and the next state in html:"""
+
+# Build messages
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": user_content},
+        ],
+    }
+]
+
+# Apply chat template
+prompt = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+# Generation parameters
+sampling_params = SamplingParams(
+    max_tokens=15000,
+    temperature=0,
+    seed=42,
+    top_p=1.0,
+)
+
+# Generate
+outputs = llm.generate(
+    [{"prompt": prompt, "multi_modal_data": {"image": image}}],
+    sampling_params=sampling_params
+)
+
+print(outputs[0].outputs[0].text)
+```
 
 ## License and Contact
 This model is licensed under the Apache License 2.0. For inquiries, please contact: info@trillionlabs.co
 
-
 ## Citation
-```
+```bibtex
 @misc{koh2026generativevisualcodemobile,
-      title={Generative Visual Code Mobile World Models},
-      author={Woosung Koh and Sungjun Han and Segyu Lee and Se-Young Yun and Jamin Shin},
-      year={2026},
-      eprint={2602.01576},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2602.01576},
+      title={Generative Visual Code Mobile World Models},
+      author={Woosung Koh and Sungjun Han and Segyu Lee and Se-Young Yun and Jamin Shin},
+      year={2026},
+      eprint={2602.01576},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.01576},
 }
 ```