Add pipeline tag and library name to metadata

This PR updates the model card metadata by adding the `pipeline_tag: image-text-to-text` and `library_name: transformers`.

Adding these tags will:
1. Improve the model's discoverability on the Hugging Face Hub under the [Image-Text-to-Text](https://huggingface.co/models?pipeline_tag=image-text-to-text) category.
2. Enable the "Use in Transformers" button on the model page, providing users with a ready-to-use code snippet.

The sample usage provided in the documentation confirms that the model is designed to work with the `transformers` library.

README.md

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 base_model:
-  - stepfun-ai/Step3-VL-10B-Base
+- stepfun-ai/Step3-VL-10B-Base
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
 ---
 
 <div align="center">
@@ -57,8 +59,6 @@ STEP3-VL-10B delivers best-in-class performance across major multimodal benchmar
 | **HMMT 2025**     |        78.18        |       **92.14**       |        57.29         |        67.71         |     65.68      |    51.30    |
 | **LiveCodeBench** |        75.77        |       **76.43**       |        48.71         |        69.45         |     72.01      |    57.10    |
 
-<!-- > **Note:** **SeRe** (Sequential Reasoning) uses a max length of 64K tokens; **PaCoRe** (Parallel Coordinated Reasoning) synthesizes 16 SeRe rollouts with a max length of 128K tokens. -->
-
 > **Note on Inference Modes:**
 >
 > **SeRe (Sequential Reasoning):** The standard inference mode using sequential generation (Chain-of-Thought) with a max length of 64K tokens.
@@ -120,7 +120,7 @@ STEP3-VL-10B delivers best-in-class performance across major multimodal benchmar
 
 ### Inference with Hugging Face Transformers
 
-We introduce how to use our model at inference stage using transformers library. It is recommended to use python=3.10, torch>=2.1.0, and transformers=4.57.0 as the development environment.We currently only support bf16 inference, and multi-patch for image preprocessing is supported by default. This behavior is aligned with vllm and sglang.
+We introduce how to use our model at inference stage using transformers library. It is recommended to use python=3.10, torch>=2.1.0, and transformers=4.57.0 as the development environment. We currently only support bf16 inference, and multi-patch for image preprocessing is supported by default. This behavior is aligned with vllm and sglang.
 
 ```python
 from transformers import AutoProcessor, AutoModelForCausalLM
@@ -185,4 +185,4 @@ If you find this project useful in your research, please cite our technical repo
 
 ## 📄 License
 
-This project is open-sourced under the [Apache 2.0 License](https://www.google.com/search?q=LICENSE).
+This project is open-sourced under the [Apache 2.0 License](https://www.google.com/search?q=LICENSE).