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 Settings

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

prithivMLmods commited on Mar 24

Commit

0db4c3f

verified ·

1 Parent(s): 07b9650

update

Browse files

Files changed (1) hide show

chat_template.jinja +20 -6

chat_template.jinja CHANGED Viewed

@@ -1,5 +1,10 @@
 {%- set image_count = namespace(value=0) %}
 {%- set video_count = namespace(value=0) %}
 {%- macro render_content(content, do_vision_count, is_system_content=false) %}
     {%- if content is string %}
         {{- content }}
@@ -39,12 +44,15 @@
         {{- raise_exception('Unexpected content type.') }}
     {%- endif %}
 {%- endmacro %}
 {%- if not messages %}
     {{- raise_exception('No messages provided.') }}
 {%- endif %}
 {%- if tools and tools is iterable and tools is not mapping %}
     {{- '<|im_start|>system\n' }}
-    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
     {%- for tool in tools %}
         {{- "\n" }}
         {{- tool | tojson }}
@@ -60,10 +68,13 @@
     {{- '<|im_end|>\n' }}
 {%- else %}
     {%- if messages[0].role == 'system' %}
-        {%- set content = render_content(messages[0].content, false, true)|trim %}
-        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
     {%- endif %}
 {%- endif %}
 {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
 {%- for message in messages[::-1] %}
     {%- set index = (messages|length - 1) - loop.index0 %}
@@ -75,9 +86,11 @@
         {%- endif %}
     {%- endif %}
 {%- endfor %}
 {%- if ns.multi_step_tool %}
     {{- raise_exception('No user query found in messages.') }}
 {%- endif %}
 {%- for message in messages %}
     {%- set content = render_content(message.content, true)|trim %}
     {%- if message.role == "system" %}
@@ -144,11 +157,12 @@
         {{- raise_exception('Unexpected message role.') }}
     {%- endif %}
 {%- endfor %}
 {%- if add_generation_prompt %}
     {{- '<|im_start|>assistant\n' }}
-    {%- if enable_thinking is defined and enable_thinking is false %}
-        {{- '<think>\n\n</think>\n\n' }}
-    {%- else %}
         {{- '<think>\n' }}
     {%- endif %}
 {%- endif %}

 {%- set image_count = namespace(value=0) %}
 {%- set video_count = namespace(value=0) %}
+{%- set detection_system_prompt %}
+You are a helpful assistant to detect objects in images. When asked to detect elements based on a description you return bounding boxes for all elements in the form of [xmin, ymin, xmax, ymax] with the values being scaled between 0 and 1000. When there are more than one result, answer with a list of bounding boxes in the form of [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ...].
+{%- endset %}
 {%- macro render_content(content, do_vision_count, is_system_content=false) %}
     {%- if content is string %}
         {{- content }}
         {{- raise_exception('Unexpected content type.') }}
     {%- endif %}
 {%- endmacro %}
 {%- if not messages %}
     {{- raise_exception('No messages provided.') }}
 {%- endif %}
 {%- if tools and tools is iterable and tools is not mapping %}
     {{- '<|im_start|>system\n' }}
+    {{- detection_system_prompt }}
+    {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
     {%- for tool in tools %}
         {{- "\n" }}
         {{- tool | tojson }}
     {{- '<|im_end|>\n' }}
 {%- else %}
     {%- if messages[0].role == 'system' %}
+        {%- set user_system = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + detection_system_prompt + '\n\n' + user_system + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\n' + detection_system_prompt + '<|im_end|>\n' }}
     {%- endif %}
 {%- endif %}
 {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
 {%- for message in messages[::-1] %}
     {%- set index = (messages|length - 1) - loop.index0 %}
         {%- endif %}
     {%- endif %}
 {%- endfor %}
 {%- if ns.multi_step_tool %}
     {{- raise_exception('No user query found in messages.') }}
 {%- endif %}
 {%- for message in messages %}
     {%- set content = render_content(message.content, true)|trim %}
     {%- if message.role == "system" %}
         {{- raise_exception('Unexpected message role.') }}
     {%- endif %}
 {%- endfor %}
 {%- if add_generation_prompt %}
     {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
         {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
     {%- endif %}
 {%- endif %}