Image-Text-to-Text
Transformers
Safetensors
internvl_chat
feature-extraction
conversational
custom_code

Improve model card: Add pipeline tag, library name, and code link

#1
by nielsr HF Staff - opened
Files changed (1) hide show
  1. README.md +6 -4
README.md CHANGED
@@ -1,10 +1,12 @@
1
  ---
2
- license: apache-2.0
 
3
  datasets:
4
  - Code2Logic/GameQA-140K
5
  - Code2Logic/GameQA-5K
6
- base_model:
7
- - OpenGVLab/InternVL2_5-8B
 
8
  ---
9
 
10
  ***This model (GameQA-InternVL2.5-8B) results from training InternVL2.5-8B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
@@ -19,7 +21,7 @@ base_model:
19
 
20
  This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
21
 
22
- [[πŸ“– Paper](https://arxiv.org/abs/2505.13886)] [[πŸ€— GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[πŸ€— GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[πŸ€— GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[πŸ€— GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[πŸ€— GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
23
 
24
  <div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
25
 
 
1
  ---
2
+ base_model:
3
+ - OpenGVLab/InternVL2_5-8B
4
  datasets:
5
  - Code2Logic/GameQA-140K
6
  - Code2Logic/GameQA-5K
7
+ license: apache-2.0
8
+ pipeline_tag: image-text-to-text
9
+ library_name: transformers
10
  ---
11
 
12
  ***This model (GameQA-InternVL2.5-8B) results from training InternVL2.5-8B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
 
21
 
22
  This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
23
 
24
+ [[πŸ“– Paper](https://arxiv.org/abs/2505.13886)] [[πŸ’» Code](https://github.com/tongjingqi/Code2Logic)] [[πŸ€— GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[πŸ€— GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[πŸ€— GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[πŸ€— GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[πŸ€— GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
25
 
26
  <div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
27