Instructions to use CalmState/Qwen-3-4b-Polyglot-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CalmState/Qwen-3-4b-Polyglot-r2 with Transformers:

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

pipe = pipeline("text-generation", model="CalmState/Qwen-3-4b-Polyglot-r2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CalmState/Qwen-3-4b-Polyglot-r2")
model = AutoModelForCausalLM.from_pretrained("CalmState/Qwen-3-4b-Polyglot-r2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CalmState/Qwen-3-4b-Polyglot-r2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CalmState/Qwen-3-4b-Polyglot-r2"
# 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/Qwen-3-4b-Polyglot-r2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CalmState/Qwen-3-4b-Polyglot-r2

SGLang

How to use CalmState/Qwen-3-4b-Polyglot-r2 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/Qwen-3-4b-Polyglot-r2" \
    --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/Qwen-3-4b-Polyglot-r2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Qwen-3-4b-Polyglot-r2" \
        --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/Qwen-3-4b-Polyglot-r2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use CalmState/Qwen-3-4b-Polyglot-r2 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 CalmState/Qwen-3-4b-Polyglot-r2 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 CalmState/Qwen-3-4b-Polyglot-r2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CalmState/Qwen-3-4b-Polyglot-r2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CalmState/Qwen-3-4b-Polyglot-r2",
    max_seq_length=2048,
)

Docker Model Runner
How to use CalmState/Qwen-3-4b-Polyglot-r2 with Docker Model Runner:
```
docker model run hf.co/CalmState/Qwen-3-4b-Polyglot-r2
```

Polyglot r2

This model is a specialized fine-tune of Qwen-3 4b, optimized specifically for the Polyglot Air desktop application workflow. This version was trained on a dataset approximately 4 times larger than its predecessor, significantly expanding its range of commands and improving the accuracy of its responses.

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.

Translation

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 (Simplified)	`Hello::zh`	`你好`

Tone & Style Adjustment

Suffix	Action	Example Input	Model Output
`::fix`	Fix spelling and grammar	`i goes to skool yesterday::fix`	`I went to school yesterday`
`::formal`	Rewrite in a professional/formal tone	`Hey, send me that file asap::formal`	`Could you please send me that file as soon as possible?`
`::casual`	Rewrite in a casual tone	`I acknowledge receipt of your message::casual`	`Got your message, thanks!`
`::informal`	Rewrite in a slang/informal tone	`That is very interesting::informal`	`That's pretty cool`
`::polite`	Make polite	`I need the report now.::polite`	`Could you please provide the report when you have a moment?`
`::technical`	Make technical	`The computer is broken.::technical`	`The CPU is experiencing a critical hardware failure.`
`::creative`	Make creative	`The sun set.::creative`	`The sun painted the sky in hues of fire and gold.`
`::business`	Make business-oriented	`We should sell more.::business`	`We must optimize our sales funnel to increase revenue.`
`::news`	Rewrite in a news style	`A car crashed on the main street.::news`	`A vehicular collision occurred on Main Street earlier today.`
`::social`	Rewrite in a social media style	`I'm launching a new product.::social`	`🔥 BIG NEWS! So excited to launch our new product! #launch #new`

Content & Structure Manipulation

Suffix	Action	Example Input	Model Output
`::summarize`	Summarize the text	`[Long Text]::summarize`	`[Concise Summary]`
`::expand`	Expand / add details	`The project was a success.::expand`	`The project was a resounding success, exceeding all initial expectations.`
`::elaborate`	Elaborate / add details	`She was happy.::elaborate`	`A wide smile spread across her face, and her eyes sparkled with joy.`
`::simplify`	Simplify the text	`The confluence of factors precipitated this.::simplify`	`Many things caused this to happen.`
`::concise`	Make concise	`Due to the fact that it was raining, we left.::concise`	`We left because it was raining.`
`::toQuestion`	Transform into a question	`The report is due tomorrow.::toQuestion`	`Is the report due tomorrow?`
`::toStatement`	Transform into a statement	`Should we start the project?::toStatement`	`We should start the project.`