Improve model card: add paper info, repository link, and description

README.md

@@ -1,58 +1,93 @@
 ---
+base_model: zai-org/Glyph
 library_name: transformers
 license: other
-base_model: zai-org/Glyph
+pipeline_tag: image-text-to-text
 tags:
 - llama-factory
 - full
 - generated_from_trainer
+- vision-language-model
+- reasoning
 model-index:
 - name: vtc-r1-glyph
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
+# VTC-R1-Glyph
 
-## Model description
+VTC-R1 (Vision-Text Compression for Efficient Long-Context Reasoning) is an efficient reasoning paradigm that integrates vision-text compression into the reasoning process. This repository contains the fine-tuned version of [zai-org/Glyph](https://huggingface.co/zai-org/Glyph) (based on GLM-4V) using this paradigm.
 
-More information needed
+- **Paper:** [VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning](https://huggingface.co/papers/2601.22069)
+- **Repository:** [https://github.com/w-yibo/VTC-R1](https://github.com/w-yibo/VTC-R1)
 
-## Intended uses & limitations
+## Model Description
 
-More information needed
+VTC-R1 addresses efficiency bottlenecks in long-context reasoning for Vision-Language Models (VLMs). Instead of processing lengthy textual traces, VTC-R1 renders intermediate reasoning segments into compact images, which are iteratively fed back into the model as "optical memory." 
 
-## Training and evaluation data
+Key features:
+- **Efficiency:** Achieves 3.4x token compression and 2.7x speedup in end-to-end latency.
+- **Performance:** Outperforms standard long-context reasoning on benchmarks like MATH500, AIME25, AMC23, and GPQA-D.
+- **Scalability:** Integrates vision-text compression directly into the reasoning process without needing external compression models.
 
-More information needed
+## Setup & Inference
 
-## Training procedure
+### Installation
+To use this model, install the required dependencies:
+```bash
+apt-get install poppler-utils # or conda install -c conda-forge poppler
+pip install torch==2.6.0
+pip install transformers==4.57.1 
+pip install reportlab
+pip install pdf2image 
+```
 
-### Training hyperparameters
+### Inference
+You can run the inference code provided in the [official repository](https://github.com/w-yibo/VTC-R1) to generate VTC-R1 style reasoning:
+```bash
+python inference.py # replace your model path in the script
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 64
-- total_eval_batch_size: 64
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 1
-
-### Training results
+## Training Procedure
 
+The model was fine-tuned using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) on a dataset derived from OpenR1-Math-220K.
 
+### Training Hyperparameters
 
-### Framework versions
+The following hyperparameters were used during training:
+- **learning_rate:** 1e-05
+- **train_batch_size:** 1
+- **eval_batch_size:** 8
+- **seed:** 42
+- **distributed_type:** multi-GPU
+- **num_devices:** 8
+- **gradient_accumulation_steps:** 8
+- **total_train_batch_size:** 64
+- **total_eval_batch_size:** 64
+- **optimizer:** AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- **lr_scheduler_type:** cosine
+- **lr_scheduler_warmup_ratio:** 0.1
+- **num_epochs:** 1
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@misc{wang2026vtcr1visiontextcompressionefficient,
+      title={VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning}, 
+      author={Yibo Wang and Yongcheng Jing and Shunyu Liu and Hao Guan and Rong-cheng Tu and Chengyu Wang and Jun Huang and Dacheng Tao},
+      year={2026},
+      eprint={2601.22069},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.22069}, 
+}
+```
+
+## Framework Versions
 
 - Transformers 4.57.1
 - Pytorch 2.6.0+cu124
 - Datasets 4.0.0
-- Tokenizers 0.22.1
+- Tokenizers 0.22.1