huggingface--transformers/docs/source/en/model_doc/qwen3.md

This model was published in HF papers on 2025-05-14 and contributed to Hugging Face Transformers on 2025-03-31.

Qwen3

Qwen3 is the dense model architecture in the Qwen3 family, available in sizes from 0.6B to 32B parameters. It supports both thinking mode (multi-step reasoning) and non-thinking mode, with seamless switching between the two. Qwen3 was trained on approximately 36T tokens covering 119 languages. See also the MoE variant Qwen3MoE.

The example below demonstrates how to generate text with [Pipeline] or the [AutoModelForCausalLM] class.

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="Qwen/Qwen3-0.6B",
)
pipe("The key to effective reasoning is")

from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    device_map="auto",
)
input_ids = tokenizer("The key to effective reasoning is", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Qwen3Config

autodoc Qwen3Config

Qwen3Model

autodoc Qwen3Model - forward

Qwen3ForCausalLM

autodoc Qwen3ForCausalLM - forward

Qwen3ForSequenceClassification

autodoc Qwen3ForSequenceClassification - forward

Qwen3ForTokenClassification

autodoc Qwen3ForTokenClassification - forward

Qwen3ForQuestionAnswering

autodoc Qwen3ForQuestionAnswering - forward