Instructions to use CalmState/gemma-3-4b-polyglot-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CalmState/gemma-3-4b-polyglot-r1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="CalmState/gemma-3-4b-polyglot-r1")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CalmState/gemma-3-4b-polyglot-r1")
model = AutoModelForMultimodalLM.from_pretrained("CalmState/gemma-3-4b-polyglot-r1", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CalmState/gemma-3-4b-polyglot-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CalmState/gemma-3-4b-polyglot-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CalmState/gemma-3-4b-polyglot-r1",
		"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/CalmState/gemma-3-4b-polyglot-r1

SGLang

How to use CalmState/gemma-3-4b-polyglot-r1 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 "CalmState/gemma-3-4b-polyglot-r1" \
    --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": "CalmState/gemma-3-4b-polyglot-r1",
		"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 "CalmState/gemma-3-4b-polyglot-r1" \
        --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": "CalmState/gemma-3-4b-polyglot-r1",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use CalmState/gemma-3-4b-polyglot-r1 with Docker Model Runner:
```
docker model run hf.co/CalmState/gemma-3-4b-polyglot-r1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Polyglot Air - Specialized Gemma 3 Fine-Tune

This model is a specialized fine-tune of Google's Gemma 3 4B, optimized specifically for the Polyglot Air desktop application workflow.

It has been trained to strictly adhere to suffix-based commands, ensuring instant, clean, and direct responses without conversational filler, hallucinations, or "Here is the translation" preambles.

🎯 Purpose

The default chat models often struggle with context switching when presented with short strings followed by a command (e.g., text::en). They might explain the translation or chat with the user.

This model is fine-tuned to treat the ::suffix as a strict instruction code.

Input: O arquivo não pode ser salvo porque o disco está cheio::en
Output: The file cannot be saved because the disk is full

⚡ Suffix Command Table

Use these suffixes at the end of your selected text to trigger specific transformations.

Suffix	Action	Example Input	Model Output
`::en`	Translate to English	`Bom dia::en`	`Good morning`
`::pt`	Translate to Portuguese (Portugal)	`Good morning::pt`	`Bom dia`
`::ptbr`	Translate to Portuguese (Brazil)	`The bus is coming::ptbr`	`O ônibus está vindo`
`::es`	Translate to Spanish	`Hello friend::es`	`Hola amigo`
`::zh`	Translate to Chinese	`Hello::zh`	`你好`
`::fix`	Fix grammar & spelling	`i goes to skool yesterday::fix`	`I went to school yesterday`
`::formal`	Rewrite in a professional tone	`Hey, send me that file asap::formal`	`Could you please send me that file as soon as possible?`
`::casual`	Rewrite in a casual/friendly tone	`I acknowledge receipt of your message::casual`	`Got your message, thanks!`
`::summary`	Summarize the text	`[Long Text]::summary`	`[Concise Summary]`