huggingface--transformers/docs/source/en/model_doc/qwen3_next.md

This model was contributed to Hugging Face Transformers on 2025-09-10.

Overview

The Qwen3-Next series represents our next-generation foundation models, optimized for extreme context length and large-scale parameter efficiency. The series introduces a suite of architectural innovations designed to maximize performance while minimizing computational cost:

• Hybrid Attention: Replaces standard attention with the combination of Gated DeltaNet and Gated Attention, enabling efficient context modeling.
• High-Sparsity MoE: Achieves an extreme low activation ratio as 1:50 in MoE layers — drastically reducing FLOPs per token while preserving model capacity.
• Multi-Token Prediction(MTP): Boosts pretraining model performance, and accelerates inference.
• Other Optimizations: Includes techniques such as zero-centered and weight-decayed layernorm, Gated Attention, and other stabilizing enhancements for robust training.

Built on this architecture, we trained and open-sourced Qwen3-Next-80B-A3B — 80B total parameters, only 3B active — achieving extreme sparsity and efficiency.

Despite its ultra-efficiency, it outperforms Qwen3-32B on downstream tasks — while requiring less than 1/10 of the training cost. Moreover, it delivers over 10x higher inference throughput than Qwen3-32B when handling contexts longer than 32K tokens.

For more details, please visit our blog Qwen3-Next (blog post).

Usage examples

from transformers import AutoModelForCausalLM, AutoTokenizer


model_name = "Qwen/Qwen3-Next-80B-A3B-Instruct"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

Qwen3NextConfig

autodoc Qwen3NextConfig

Qwen3NextModel

autodoc Qwen3NextModel - forward

Qwen3NextForCausalLM

autodoc Qwen3NextForCausalLM - forward

Qwen3NextForSequenceClassification

autodoc Qwen3NextForSequenceClassification - forward

Qwen3NextForQuestionAnswering

autodoc Qwen3NextForQuestionAnswering - forward

Qwen3NextForTokenClassification

autodoc Qwen3NextForTokenClassification - forward