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Model: gsarch/ViGoRL-7b-Spatial Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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pipeline_tag: image-text-to-text
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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---
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# ViGoRL: Visually Grounded Reinforcement Learning for Visual Reasoning
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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).
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**Authors:** Gabriel Sarch, Snigdha Saha, Naitik Khandelwal, Ayush Jain, Michael J. Tarr, Aviral Kumar, Katerina Fragkiadaki
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---
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## Model Overview
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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.
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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).
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---
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## Model Details
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* **Base Architecture:** Qwen2.5-Vision-Language (3B or 7B parameters)
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* **Training Paradigm:**
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* Supervised Fine-Tuning on MCTS-generated reasoning traces
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* Group Relative Policy Optimization (GRPO)
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* Multi-turn visual grounding with dynamic zoom-in feedback (if "Multiturn" appears in name)
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---
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## Use Cases
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This model excels in visual reasoning tasks that require precise visual grounding and region-level reasoning. Please see model name for specific domain.
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* **Spatial Reasoning:** SAT-2, BLINK, RoboSpatial
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* **Visual Search:** V\*Bench
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* **Web Interaction and Grounding:** ScreenSpot (Pro and V2), VisualWebArena
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---
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## Usage
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You can load this model easily using Hugging Face's Transformers library:
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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import torch
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# # default: Load the model on the available device(s)
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# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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# "", torch_dtype="auto", device_map="auto"
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# ) # replace with any of the ViGoRL models
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# We recommend enabling flash_attention_2 for better acceleration and memory saving.
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"",
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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device_map="auto",
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)
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# default processer
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processor = AutoProcessor.from_pretrained("")
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# The default range for the number of visual tokens per image in the model is 4-16384.
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# 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.
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# min_pixels = 256*28*28
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# max_pixels = 1280*28*28
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# processor = AutoProcessor.from_pretrained("", min_pixels=min_pixels, max_pixels=max_pixels)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "path/to/image.png",
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},
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{"type": "text", "text": "QUERY HERE"},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=512)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text) # this will output a single tool call turn of the model if version is multiturn.
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```
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**Important**: This model requires a system prompt for proper usage. Please see the model's chat template for details.
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---
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## Datasets and Training Data
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Training datasets and generated reasoning chains are publicly available:
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* [Code](https://github.com/Gabesarch/grounded-rl)
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* [ViGoRL Datasets on Hugging Face](https://huggingface.co/datasets/gsarch/vigorl_datasets)
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---
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## Citation
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If you use ViGoRL in your research or applications, please cite our paper:
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```bibtex
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@article{sarch2025vigorl,
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title={Grounded Reinforcement Learning for Visual Reasoning},
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author={Sarch, Gabriel and Saha, Snigdha and Khandelwal, Naitik and Jain, Ayush and Tarr, Michael J and Kumar, Aviral and Fragkiadaki, Katerina},
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year={2025}
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}
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```
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---
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## Contact
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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).
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---
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nA conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant systematically reasons through the problem step by step by checking and verifying possible solutions and image regions, while grounding reasoning steps to specific objects and their relationships in the image using (x,y) coordinates. There may be one image or two images concatenated together, in which case the Assistant must compare the spatial relationships between the two images.\n\nAll reasoning processes must be enclosed within a single set of '<think>' tags, and reasoning steps must include specific reference coordinates:\n\nFor example, <think>\n{Reasoning text}. {Further reasoning text} {more reasoning} \n</think>\n\nThe final answer should be enclosed in '<answer>' tags in the format:\n<answer> {text of selected answer choice} </answer>\n\nThe Assistant must help the user identify the correct answer choice from the options provided.\n-If the correct answer is unclear, select the most relevant option based on the spatial relationships and dynamics within the image.\n- The Assistant should verify each step and check multiple possible solutions before selecting the final answer.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2_5_VLForConditionalGeneration"
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"max_position_embeddings": 128000,
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"max_window_layers": 28,
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"model_type": "qwen2_5_vl",
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"in_channels": 3,
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"in_chans": 3,
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"model_type": "qwen2_5_vl",
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"num_heads": 16,
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"out_hidden_size": 3584,
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"patch_size": 14,
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"spatial_merge_size": 2,
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"spatial_patch_size": 14,
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"temporal_patch_size": 2,
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"tokens_per_second": 2,
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"torch_dtype": "bfloat16",
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}
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}
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36
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31
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Normal file
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211
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|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 8192,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"use_fast": true
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user