Instructions to use prithivMLmods/Polaris-VGA-4B-Post1.0e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Polaris-VGA-4B-Post1.0e")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/Polaris-VGA-4B-Post1.0e")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Polaris-VGA-4B-Post1.0e")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Polaris-VGA-4B-Post1.0e",
	filename="GGUF/Polaris-VGA-4B-Post1.0e.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Use Docker

docker model run hf.co/prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Polaris-VGA-4B-Post1.0e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Polaris-VGA-4B-Post1.0e",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

SGLang

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Polaris-VGA-4B-Post1.0e" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Polaris-VGA-4B-Post1.0e",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Polaris-VGA-4B-Post1.0e" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Polaris-VGA-4B-Post1.0e",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Ollama:
```
ollama run hf.co/prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
```

Unsloth Studio

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Polaris-VGA-4B-Post1.0e to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Polaris-VGA-4B-Post1.0e to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Polaris-VGA-4B-Post1.0e to start chatting

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16
```

Lemonade

How to use prithivMLmods/Polaris-VGA-4B-Post1.0e with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Polaris-VGA-4B-Post1.0e:BF16

Run and chat with the model

lemonade run user.Polaris-VGA-4B-Post1.0e-BF16

List all available models

lemonade list

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

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-Qwen3.5-4B-abliterated.

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.