How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tachyphylaxis/Smoothie-Qwen3-235B-A22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tachyphylaxis/Smoothie-Qwen3-235B-A22B")
model = AutoModelForMultimodalLM.from_pretrained("tachyphylaxis/Smoothie-Qwen3-235B-A22B")
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]:]))
Quick Links

Smoothie Qwen

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/Qwen3-235B-A22B
  • 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
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