Update README.md

README.md

@@ -1,56 +1,15 @@
 ---
-datasets: boot_osworld_vscode
+datasets:
+- OS-Copilot/ScienceBoard-Traj
 library_name: transformers
 tags:
 - generated_from_trainer
 - R1-V
 licence: license
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-VL-32B-Instruct
+pipeline_tag: image-text-to-text
 ---
 
-# Model Card for None
-
-This model is a fine-tuned version of [None](https://huggingface.co/None) on the [boot_osworld_vscode](https://huggingface.co/datasets/boot_osworld_vscode) dataset.
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
- 
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 0.17.0
-- Transformers: 4.51.3
-- Pytorch: 2.6.0
-- Datasets: 3.5.1
-- Tokenizers: 0.21.1
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+Check out our [repo](https://github.com/OpenIXCLab/CODA) and [paper](https://arxiv.org/abs/2508.20096) for more implementation details!