Polaris-VGA-2B-Post1.0e

Polaris-VGA-2B-Post1.0e is an experimental post-optimized evolution built on top of Qwen/Qwen3.5-4B, designed to extend compact-to-mid scale language modeling into the domain of VGA (Visual Grounding Anything). This variant introduces enhanced multimodal alignment and deeper visual reasoning capabilities, enabling the model to interpret complex scenes, explain visual content with higher contextual awareness, and perform precise grounding across diverse inputs. As an experimental release, it explores advanced post-training strategies to strengthen the connection between textual instructions and visual elements for detection, explanation, and structured interpretation tasks, while leveraging the expanded capacity of a 4B-scale backbone.

Visual-Grounding-Anything (code) - https://huggingface.co/prithivMLmods/Polaris-VGA-4B-Post1.0e/tree/main/Visual-Grounding-Anything

Key Highlights

Experimental VGA Optimization (e Variant): Introduces exploratory training and post-optimization strategies focused on improving grounding fidelity and reasoning depth.
VGA (Visual Grounding Anything) Specialization: Aligns textual queries with visual elements across diverse and complex environments.
Enhanced Multimodal Reasoning: Improved capability to connect scene understanding with instruction-based outputs.
Advanced Scene Interpretation: Better handling of object relationships, spatial awareness, and contextual reasoning.
Object & Point Tracking Optimization: Supports video-based workflows including object tracking and fine-grained point tracking across frames.
4B-Based Backbone Efficiency: Built on a stronger base model to improve performance while maintaining practical deployment flexibility.

Get GGUF

File Name	Quant Type	File Size	File Link
Polaris-VGA-4B-Post1.0e.BF16.gguf	BF16	8.42 GB	Download
Polaris-VGA-4B-Post1.0e.F16.gguf	F16	8.42 GB	Download
Polaris-VGA-4B-Post1.0e.F32.gguf	F32	16.8 GB	Download
Polaris-VGA-4B-Post1.0e.Q8_0.gguf	Q8_0	4.48 GB	Download
Polaris-VGA-4B-Post1.0e.mmproj-bf16.gguf	mmproj-bf16	676 MB	Download
Polaris-VGA-4B-Post1.0e.mmproj-f16.gguf	mmproj-f16	676 MB	Download
Polaris-VGA-4B-Post1.0e.mmproj-f32.gguf	mmproj-f32	1.33 GB	Download
Polaris-VGA-4B-Post1.0e.mmproj-q8_0.gguf	mmproj-q8_0	367 MB	Download

Recommended (chat_template.jinja) - https://huggingface.co/prithivMLmods/Polaris-VGA-4B-Post1.0e/blob/main/chat_template.jinja

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Polaris-VGA-4B-Post1.0e/blob/main/standard-chat_template/chat_template.jinja

Download the model

hf auth login --token <YOUR_HF_TOKEN>

hf download prithivMLmods/Polaris-VGA-4B-Post1.0e

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-4B-Post1.0e",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Polaris-VGA-4B-Post1.0e"
)

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

Experimental Multimodal Research: Exploring advanced visual grounding and reasoning behaviors.
Scene Understanding Systems: Interpreting and explaining complex visual environments.
Video Analysis & Tracking Research: Prototyping object tracking and point tracking pipelines.
Multimodal Alignment Studies: Investigating how language models interact with visual representations.
Rapid Prototyping: Testing new ideas on a moderately scaled multimodal architecture.

Capabilities

Visual Scene Understanding: Interprets diverse scenes for reasoning, detection, and descriptive tasks.
Cross-Modal Reasoning: Bridges textual instructions with visual data for grounded outputs.
Detection-Oriented Tasks: Identifies, localizes, and contextualizes visual elements.
Tracking-Oriented Tasks: Maintains object and point consistency across sequential frames.
General Visual Explanation: Explains “anything” visible in an input with structured and coherent responses.

Limitations

Important Note: This is an experimental variant focused on expanding multimodal grounding capabilities.

Experimental Stability: As an experimental release, outputs may vary across edge cases and complex scenarios.
Moderate Scale Trade-offs: While based on a 4B backbone, it may still fall short of larger systems in highly demanding reasoning tasks.
Visual Ambiguity Sensitivity: Performance depends on clarity and complexity of visual inputs.
User Responsibility: Outputs should be used responsibly, especially in sensitive or high-impact applications.