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="Stormtrooperaim/UltraThinker-1.7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Stormtrooperaim/UltraThinker-1.7b")
model = AutoModelForCausalLM.from_pretrained("Stormtrooperaim/UltraThinker-1.7b")
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]:]))
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UltraThinker-1.7b 🧠

UltraThinker-1.7b is a fine-tuned version of Qwen/Qwen3-1.7B designed to improve reasoning and output quality. Compared to the base 1.7 billion-parameter model, it’s optimized to “think better” — meaning it produces more coherent, context-aware responses and shows stronger problem-solving and understanding skills in general-purpose tasks.

  • Increasing the context window seems to improve the model's performance.

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