Polaris-VGA-2B-Post1.0

Polaris-VGA-2B-Post1.0 is a post-optimized evolution built on top of Qwen/Qwen3.5-2B, designed to extend compact language modeling into the domain of VGA (Visual Grounding Anything). This model integrates advanced visual understanding with strong instruction-following capabilities, enabling it to interpret complex scenes, explain visual content in depth, and perform grounding across diverse inputs. Through targeted post-training optimizations, it enhances multimodal reasoning, allowing precise alignment between textual instructions and visual elements for detection, explanation, and structured interpretation tasks, while leveraging the increased capacity of a 2B parameter architecture for improved performance and reasoning depth.

Visual-Grounding-Anything (code) - https://huggingface.co/prithivMLmods/Polaris-VGA-2B-Post1.0/tree/main/Visual-Grounding-Anything

Key Highlights

• VGA (Visual Grounding Anything) Specialization: Designed to associate textual queries with visual elements across a wide range of scenes and contexts.
• Post-Optimized Training Pipeline: Refined on top of the base model to improve multimodal alignment, reasoning, and response quality.
• Enhanced Visual Understanding: Interprets complex scenes, object relationships, and contextual cues with improved depth over smaller variants.
• Scene Explanation & Reasoning: Produces detailed, structured explanations grounded in visual inputs.
• Object & Point Tracking Optimization: Adapted for video-based tasks including object tracking and point-level tracking across frames.
• Efficient 2B Architecture: Balances stronger reasoning and multimodal capabilities with relatively low computational requirements.

Get GGUF

File Name	Quant Type	File Size	File Link
Polaris-VGA-2B-Post1.0.BF16.gguf	BF16	3.78 GB	Download
Polaris-VGA-2B-Post1.0.F16.gguf	F16	3.78 GB	Download
Polaris-VGA-2B-Post1.0.F32.gguf	F32	7.54 GB	Download
Polaris-VGA-2B-Post1.0.Q8_0.gguf	Q8_0	2.01 GB	Download
Polaris-VGA-2B-Post1.0.mmproj-bf16.gguf	mmproj-bf16	671 MB	Download
Polaris-VGA-2B-Post1.0.mmproj-f16.gguf	mmproj-f16	671 MB	Download
Polaris-VGA-2B-Post1.0.mmproj-f32.gguf	mmproj-f32	1.33 GB	Download
Polaris-VGA-2B-Post1.0.mmproj-q8_0.gguf	mmproj-q8_0	365 MB	Download

Recommended (chat_template.jinja) - https://huggingface.co/prithivMLmods/Polaris-VGA-2B-Post1.0/blob/main/chat_template.jinja

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Polaris-VGA-2B-Post1.0/blob/main/standard-chat_template/chat_template.jinja

Download the model

hf auth login --token <YOUR_HF_TOKEN>

hf download prithivMLmods/Polaris-VGA-2B-Post1.0

Quick Start with Transformers

pip install transformers==5.3.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Polaris-VGA-2B-Post1.0",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Polaris-VGA-2B-Post1.0"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image in extreme detail."}
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

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)

Intended Use

• Visual Grounding Research: Studying alignment between language and visual elements across diverse scenarios.
• Scene Understanding Applications: Analyzing and explaining visual data for downstream tasks.
• Video Analysis Prototyping: Supporting object tracking and point tracking experiments in video workflows.
• Multimodal AI Systems: Deploying visual reasoning capabilities in practical applications.
• Research & Experimentation: Prototyping with compact yet capable multimodal transformer architectures.

Capabilities

• Visual Scene Understanding: Interprets any scene for reasoning, detection, and descriptive tasks.
• Cross-Modal Reasoning: Connects visual inputs with textual instructions for grounded outputs.
• Detection-Oriented Tasks: Identifies and contextualizes objects and regions within visual data.
• Tracking-Oriented Tasks: Supports object continuity and point tracking across sequential frames.
• General Visual Explanation: Explains “anything” visible in an input with coherent and structured responses.

Limitations

Important Note: This model emphasizes broad visual grounding and reasoning within a compact architecture.

• Moderate Scale Constraints: While larger than 0.8B models, it may still underperform compared to significantly larger multimodal systems in highly complex reasoning tasks.
• Visual Ambiguity Sensitivity: Performance depends on input quality, scene clarity, and complexity.
• User Responsibility: Outputs should be used responsibly and within appropriate ethical and legal boundaries.
• Experimental Multimodal Behavior: Certain edge cases in grounding and tracking may require further refinement depending on usage scenarios.

Acknowledgements

• Huggingface Transformers: https://github.com/huggingface/transformers
• Qwen 3.5 – Towards Native Multimodal Agents: https://huggingface.co/collections/Qwen/qwen35