Improve model card: Add metadata, links, usage, and citation

This PR significantly enhances the model card by:
- Adding `pipeline_tag: image-text-to-text`, making the model discoverable under this pipeline on the Hugging Face Hub.
- Specifying `library_name: transformers` as the model's `config.json` indicates compatibility with the Transformers library, enabling the automated "how to use" widget.
- Including direct links to the paper ([Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models](https://huggingface.co/papers/2511.17487)), the project page, and the GitHub repository for easy access to resources.
- Incorporating a "Usage" section with clear `bash` code snippets directly from the original GitHub README to demonstrate model evaluation.
- Adding the BibTeX "Citation" section from the GitHub README.

Existing content, including the detailed "File information", has been retained as part of the current model card's structure.

README.md

@@ -1,9 +1,629 @@
 ---
-{}
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 # Extract+Think Model Card
 
+This repository contains the `Extract+Think` model, which explores perception and reasoning bottlenecks in small multimodal models, as presented in the paper [Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models](https://huggingface.co/papers/2511.17487).
+
+For more details, visit the [Project Page](https://web.stanford.edu/~markendo/projects/downscaling_intelligence) and the [GitHub Repository](https://github.com/markendo/downscaling_intelligence).
+
 ## Model details
 
-Extract-from-scratch-0.6B is used as the perception module for the two-stage Extract+Think<sup>†</sup> framework. This setup trains from scratch under the visual extraction tuning paradigm (after connector pre-training). 
+Extract-from-scratch-0.6B is used as the perception module for the two-stage Extract+Think<sup>†</sup> framework. This setup trains from scratch under the visual extraction tuning paradigm (after connector pre-training).
+
+## Usage
+The model utilizes a two-stage pipeline for evaluation. First, generate extracted visual information, then run the second stage of reasoning.
+
+**Setup Evaluation Framework:**
+We utilize [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) to evaluate our approach. Follow the instructions from the [GitHub repository](https://github.com/markendo/downscaling_intelligence) to set up the evaluation framework.
+
+**First Stage (Visual Extraction):**
+```bash
+cd lmms-eval
+model_name=markendo/llava-extract-qwen3-1.7B
+python -m lmms_eval \
+    --model=llava_onevision \
+    --model_args=pretrained=$model_name,conv_template=qwen_1_5,device_map=auto \
+    --tasks=mmstar_prism_stage_1 \
+    --batch_size=1 \
+    --output_path results \
+    --log_samples
+```
+
+**Second Stage (Reasoning):**
+```bash
+stage_1_path=/path/to/stage_1/samples.jsonl
+perception_model_size=1.7B
+pretrained=Qwen/Qwen3-4B
+
+enable_thinking=True
+python -m lmms_eval \
+    --model=qwen3 \
+    --model_args="pretrained=${perception_model_size};${pretrained};${enable_thinking},stage_1_path=$stage_1_path" \
+    --tasks=mmstar_prism_stage_2 \
+    --batch_size=1 \
+    --output_path results \
+    --log_samples
+```
+
+## Citation
+If this work is helpful to you, please consider citing it:
+```bib
+@article{endo2025downscalingintelligence,
+  author    = {Endo, Mark and Yeung-Levy, Serena},
+  title     = {Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models},
+  journal   = {arXiv preprint},
+  year      = {2025},
+}
+```
+
+# File information
+
+The repository contains the following file information:
+
+Filename: tokenizer.json
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+}
+
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