Polaris-VGA-9B-Post1.0e

Polaris-VGA-9B-Post1.0e is an experimental post-optimized evolution built on top of Qwen/Qwen3.5-9B, designed to extend mid-to-large scale language modeling into the domain of VGA (Visual Grounding Anything). This variant advances multimodal alignment and visual reasoning by combining a stronger backbone with targeted post-training optimizations, enabling the model to interpret highly complex scenes, generate detailed visual explanations, and perform precise grounding across diverse inputs. As an experimental “e” release, it explores enhanced strategies for aligning textual instructions with visual elements for detection, reasoning, and structured interpretation tasks, leveraging the expanded capacity of a 9B parameter architecture for deeper understanding and improved consistency.

Visual-Grounding-Anything (code) - https://huggingface.co/prithivMLmods/Polaris-VGA-9B-Post1.0e/tree/main/Visual-Grounding-Anything

Key Highlights

Experimental VGA Optimization (e Variant): Incorporates exploratory post-training techniques to improve grounding precision and reasoning consistency.
VGA (Visual Grounding Anything) Specialization: Aligns textual queries with visual elements across complex and diverse environments.
Advanced Multimodal Reasoning: Stronger capability to connect scene understanding with detailed instruction-following outputs.
Deep Scene Interpretation: Enhanced understanding of object relationships, spatial structure, and contextual cues.
Object & Point Tracking Optimization: Adapted for video workflows including object tracking and fine-grained point tracking across frames.
9B Parameter Backbone: Utilizes a larger architecture for improved reasoning depth, contextual awareness, and output quality.

Get GGUF

File Name	Quant Type	File Size	File Link
Polaris-VGA-9B-Post1.0e.BF16.gguf	BF16	17.9 GB	Download
Polaris-VGA-9B-Post1.0e.F16.gguf	F16	17.9 GB	Download
Polaris-VGA-9B-Post1.0e.F32.gguf	F32	35.8 GB	Download
Polaris-VGA-9B-Post1.0e.Q8_0.gguf	Q8_0	9.53 GB	Download
Polaris-VGA-9B-Post1.0e.mmproj-bf16.gguf	mmproj-bf16	922 MB	Download
Polaris-VGA-9B-Post1.0e.mmproj-f16.gguf	mmproj-f16	922 MB	Download
Polaris-VGA-9B-Post1.0e.mmproj-f32.gguf	mmproj-f32	1.82 GB	Download
Polaris-VGA-9B-Post1.0e.mmproj-q8_0.gguf	mmproj-q8_0	624 MB	Download

Recommended (chat_template.jinja) - https://huggingface.co/prithivMLmods/Polaris-VGA-9B-Post1.0e/blob/main/chat_template.jinja

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Polaris-VGA-9B-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-9B-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-9B-Post1.0e",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Polaris-VGA-9B-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

Advanced Multimodal Research: Exploring high-capacity visual grounding and reasoning systems.
Complex Scene Understanding: Analyzing and explaining visually dense or ambiguous environments.
Video Analysis & Tracking Systems: Supporting object tracking and point tracking in extended sequences.
Multimodal Alignment Studies: Investigating deeper interactions between language and visual representations.
Prototyping & Evaluation: Testing experimental multimodal capabilities at a larger scale.

Capabilities

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

Limitations

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

Experimental Behavior: Outputs may vary in edge cases due to ongoing optimization strategies.
Resource Requirements: Increased model size requires more computational resources compared to smaller variants.
Visual Ambiguity Sensitivity: Performance depends on input clarity and scene complexity.
User Responsibility: Outputs should be used responsibly and within appropriate ethical and legal boundaries.