Instructions to use dnotitia/Smoothie-Qwen3-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dnotitia/Smoothie-Qwen3-14B with Transformers:

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

pipe = pipeline("text-generation", model="dnotitia/Smoothie-Qwen3-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dnotitia/Smoothie-Qwen3-14B")
model = AutoModelForCausalLM.from_pretrained("dnotitia/Smoothie-Qwen3-14B", device_map="auto")
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 Settings

vLLM

How to use dnotitia/Smoothie-Qwen3-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dnotitia/Smoothie-Qwen3-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dnotitia/Smoothie-Qwen3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dnotitia/Smoothie-Qwen3-14B

SGLang

How to use dnotitia/Smoothie-Qwen3-14B 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 "dnotitia/Smoothie-Qwen3-14B" \
    --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": "dnotitia/Smoothie-Qwen3-14B",
		"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 "dnotitia/Smoothie-Qwen3-14B" \
        --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": "dnotitia/Smoothie-Qwen3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dnotitia/Smoothie-Qwen3-14B with Docker Model Runner:
```
docker model run hf.co/dnotitia/Smoothie-Qwen3-14B
```

Smoothie-Qwen3-14B / README.md

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- dnotitia
	- nlp
	- llm
	- conversation
	- chat
	- reasoning
	base_model:
	- Qwen/Qwen3-14B
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Smoothie Qwen

	<img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;">

	Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>.

	## Configuration
	- Base model: Qwen/Qwen3-14B
	- Minimum scale factor: 0.5
	- Smoothness: 10.0
	- Sample size: 1000
	- Window size: 4
	- N-gram weights: [0.5, 0.3, 0.2]

	## Unicode Ranges
	- Range 1: 0x4e00 - 0x9fff
	- Range 2: 0x3400 - 0x4dbf
	- Range 3: 0x20000 - 0x2a6df
	- Range 4: 0xf900 - 0xfaff
	- Range 5: 0x2e80 - 0x2eff
	- Range 6: 0x2f00 - 0x2fdf
	- Range 7: 0x2ff0 - 0x2fff
	- Range 8: 0x3000 - 0x303f
	- Range 9: 0x31c0 - 0x31ef
	- Range 10: 0x3200 - 0x32ff
	- Range 11: 0x3300 - 0x33ff

	## Statistics
	- Target tokens: 26,153
	- Broken tokens: 1,457
	- Modified tokens: 27,564