Update README.md

README.md

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-# ViGoRL-3b-Web-Grounding
+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: 
+- Qwen/Qwen2.5-VL-7B-Instruct
+---
 
-Checkpoint for **ViGoRL** trained on *web_grounding* domain. Uploaded automatically from `/data/group_data/katefgroup/datasets/vigorl_datasets/checkpoints/web_grounding/ViGoRL-3b-Web-Grounding`.
+# ViGoRL: Visually Grounded Reinforcement Learning for Visual Reasoning
+
+This model card describes the ViGoRL (**Vi**sually **G**r**o**unded **R**einforcement **L**earning) model, introduced in our paper ["Grounded Reinforcement Learning for Visual Reasoning"](https://arxiv.org/abs/2505.23678).
+
+**Authors:** Gabriel Sarch, Snigdha Saha, Naitik Khandelwal, Ayush Jain, Michael J. Tarr, Aviral Kumar, Katerina Fragkiadaki
+
+---
+
+## Model Overview
+
+ViGoRL is a vision-language model fine-tuned using reinforcement learning (RL) to explicitly anchor textual reasoning steps to visual coordinates. Inspired by human visual cognition, ViGoRL employs multi-turn visual grounding, dynamically zooming into image regions to perform fine-grained visual reasoning and grounding.
+
+This model was trained using supervised fine-tuning (SFT) on visually-grounded reasoning traces generated via Monte Carlo Tree Search (MCTS), followed by reinforcement learning with Group Relative Policy Optimization (GRPO).
+
+---
+
+## Model Details
+
+* **Base Architecture:** Qwen2.5-Vision-Language (3B or 7B parameters)
+* **Training Paradigm:**
+
+  * Supervised Fine-Tuning on MCTS-generated reasoning traces
+  * Group Relative Policy Optimization (GRPO)
+  * Multi-turn visual grounding with dynamic zoom-in feedback (if "Multiturn" appears in name)
+
+---
+
+## Use Cases
+
+This model excels in visual reasoning tasks that require precise visual grounding and region-level reasoning. Please see model name for specific domain.
+
+* **Spatial Reasoning:** SAT-2, BLINK, RoboSpatial
+* **Visual Search:** V\*Bench
+* **Web Interaction and Grounding:** ScreenSpot (Pro and V2), VisualWebArena
+
+---
+
+## Usage
+
+You can load this model easily using Hugging Face's Transformers library:
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# # default: Load the model on the available device(s)
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     "gsarch/ViGoRL-7b-Web-Grounding", torch_dtype="auto", device_map="auto"
+# ) # replace with any of the ViGoRL models
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving.
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "gsarch/ViGoRL-7b-Web-Grounding",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained("gsarch/ViGoRL-7b-Web-Grounding")
+
+# The default range for the number of visual tokens per image in the model is 4-16384.
+# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
+# min_pixels = 256*28*28
+# max_pixels = 1280*28*28
+# processor = AutoProcessor.from_pretrained("gsarch/ViGoRL-7b-Web-Grounding", min_pixels=min_pixels, max_pixels=max_pixels)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "path/to/image.png",
+            },
+            {"type": "text", "text": "QUERY HERE"},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=512)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text) # this will output a single tool call turn of the model if version is multiturn.
+```
+
+**Important**: This model requires a system prompt for proper usage. Please see the model's chat template for details.
+
+---
+
+## Datasets and Training Data
+
+Training datasets and generated reasoning chains are publicly available:
+
+* [Code](https://github.com/Gabesarch/grounded-rl)
+* [ViGoRL Datasets on Hugging Face](https://huggingface.co/datasets/gsarch/vigorl_datasets)
+
+---
+
+## Citation
+
+If you use ViGoRL in your research or applications, please cite our paper:
+
+```bibtex
+@article{sarch2025vigorl,
+    title={Grounded Reinforcement Learning for Visual Reasoning},
+    author={Sarch, Gabriel and Saha, Snigdha and Khandelwal, Naitik and Jain, Ayush and Tarr, Michael J and Kumar, Aviral and Fragkiadaki, Katerina},
+    year={2025}
+}
+```
+
+---
+
+## Contact
+
+For questions, feedback, or collaborations, please reach out to Gabriel Sarch or open an issue in our [GitHub repository](https://github.com/Gabesarch/grounded-rl).
+
+---