Polaris-VGA-27B-Post1.0e

Polaris-VGA-27B-Post1.0e is an experimental post-optimized evolution built on top of Qwen/Qwen3.5-27B, designed to extend large-scale language modeling into the domain of VGA (Visual Grounding Anything). This variant combines a high-capacity backbone with advanced post-training optimizations to significantly enhance multimodal alignment, enabling the model to interpret highly complex scenes, generate deeply contextual visual explanations, and perform precise grounding across diverse inputs. As part of the experimental “e” series, it explores refined strategies for aligning textual instructions with visual elements for detection, reasoning, and structured interpretation tasks, leveraging the scale of a 27B parameter architecture for superior depth, consistency, and contextual awareness.

Visual-Grounding-Anything (code) - https://huggingface.co/prithivMLmods/Polaris-VGA-27B-Post1.0e/tree/main/Visual-Grounding-Anything

Key Highlights

• Experimental VGA Optimization (e Variant): Applies advanced and exploratory post-training strategies to improve grounding accuracy and reasoning stability.
• VGA (Visual Grounding Anything) Specialization: Aligns textual queries with visual elements across highly complex and diverse scenarios.
• High-Capacity Multimodal Reasoning: Strong capability to connect detailed scene understanding with precise instruction-following outputs.
• Deep Scene Interpretation: Enhanced comprehension of object relationships, spatial hierarchies, and contextual dependencies.
• Object & Point Tracking Optimization: Designed for video workflows including object tracking and fine-grained point tracking across sequences.
• 27B Parameter Backbone: Leverages a large-scale architecture for improved reasoning depth, richer representations, and higher-quality outputs.

Get GGUF

File Name	Quant Type	File Size	File Link
Polaris-VGA-27B-Post1.0e.BF16.gguf	BF16	53.8 GB	Download
Polaris-VGA-27B-Post1.0e.F16.gguf	F16	53.8 GB	Download
Polaris-VGA-27B-Post1.0e.F32.gguf	F32	108 GB	Download
Polaris-VGA-27B-Post1.0e.Q8_0.gguf	Q8_0	28.6 GB	Download
Polaris-VGA-27B-Post1.0e.mmproj-bf16.gguf	mmproj-bf16	931 MB	Download
Polaris-VGA-27B-Post1.0e.mmproj-f16.gguf	mmproj-f16	931 MB	Download
Polaris-VGA-27B-Post1.0e.mmproj-f32.gguf	mmproj-f32	1.84 GB	Download
Polaris-VGA-27B-Post1.0e.mmproj-q8_0.gguf	mmproj-q8_0	629 MB	Download

Recommended (chat_template.jinja) - https://huggingface.co/prithivMLmods/Polaris-VGA-27B-Post1.0e/blob/main/chat_template.jinja

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Polaris-VGA-27B-Post1.0e/blob/main/standard-chat_template/chat_template.jinja

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Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.5-27B-abliterated.

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Download the model

hf auth login --token <YOUR_HF_TOKEN>

hf download prithivMLmods/Polaris-VGA-27B-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-27B-Post1.0e",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Polaris-VGA-27B-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: Investigating large-scale visual grounding and reasoning systems.
• Complex Scene Understanding: Analyzing and explaining visually dense, layered, or ambiguous environments.
• Video Analysis & Tracking Systems: Supporting object tracking and point tracking across extended sequences.
• Multimodal Alignment Studies: Exploring deep interactions between language and visual representations.
• High-End Prototyping: Developing and evaluating experimental multimodal capabilities at scale.

Capabilities

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

Limitations

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

• Experimental Behavior: Outputs may vary in edge cases due to ongoing optimization strategies.
• High Resource Requirements: The 27B model size demands substantial computational resources for inference and deployment.
• Visual Ambiguity Sensitivity: Performance may depend on input clarity and scene complexity.
• User Responsibility: Outputs should be used responsibly and within appropriate ethical and legal boundaries.

Acknowledgements

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