prithivMLmods/Polaris-VGA-0.8B-Post1.0

Polaris-VGA-0.8B-Post1.0

Polaris-VGA-0.8B-Post1.0 is a post-optimized evolution built on top of Qwen/Qwen3.5-0.8B, designed to extend compact language modeling into the domain of VGA (Visual Grounding Anything). This model integrates visual understanding with instruction-following capabilities, enabling it to interpret complex scenes, explain visual content in detail, and perform grounding across diverse inputs. Through targeted post-training optimizations, it enhances multimodal reasoning, making it capable of connecting textual instructions with visual elements for detection, explanation, and structured interpretation tasks, all while maintaining efficiency within a lightweight 0.8B parameter architecture.

[!IMPORTANT]

Visual-Grounding-Anything (code) - https://huggingface.co/prithivMLmods/Polaris-VGA-0.8B-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 and response clarity.
• —Strong Visual Understanding: Capable of interpreting complex scenes, objects, relationships, and contextual cues for reasoning tasks.
• —Scene Explanation & Reasoning: Generates detailed descriptions and structured explanations from visual inputs.
• —Object & Point Tracking Optimization: Adapted for video-based tasks including object tracking and point-level tracking across frames.
• —Efficient 0.8B Architecture: Maintains low computational requirements while extending capabilities beyond traditional text-only models.

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<details> <summary>Get GGUF</summary>

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[!NOTE]

Recommended (chat_template.jinja) - https://huggingface.co/prithivMLmods/Polaris-VGA-0.8B-Post1.0/blob/main/chat_template.jinja

[!NOTE]

Standard or Default (chat_template.jinja) – https://huggingface.co/prithivMLmods/Polaris-VGA-0.8B-Post1.0/blob/main/standard-chat_template/chat_template.jinja

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-Qwen3.5-0.8B-abliterated.

Download the model

hf auth login --token <YOUR_HF_TOKEN>

hf download prithivMLmods/Polaris-VGA-0.8B-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-0.8B-Post1.0",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Polaris-VGA-0.8B-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 how language models connect text with visual elements across diverse scenarios.
• —Scene Understanding Applications: Explaining and analyzing images or visual data for downstream tasks.
• —Video Analysis Prototyping: Supporting object tracking and point tracking experiments in video pipelines.
• —Lightweight Multimodal Systems: Deploying visual reasoning capabilities on resource-constrained environments.
• —Research & Experimentation: Rapid prototyping with compact multimodal transformer architectures.

Capabilities

• —Visual Scene Understanding: Interprets any scene for reasoning, detection, and descriptive tasks.
• —Cross-Modal Reasoning: Bridges 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 structured and coherent responses.

Limitations

Important Note: This model emphasizes broad visual understanding and grounding over scale.

• —Compact Model Constraints: As a 0.8B parameter model, depth of reasoning and long-chain inference may be limited compared to larger systems.
• —Visual Ambiguity Sensitivity: Performance may vary depending on input clarity and scene complexity.
• —User Responsibility: Outputs should be interpreted responsibly, especially in sensitive or high-stakes applications.
• —Experimental Multimodal Behavior: Some edge cases in grounding and tracking may require further refinement depending on deployment context.

Acknowledgements

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