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*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](qwen3_next) ([blog post](https://qwenlm.github.io/blog/qwen3_next/)).

## Usage examples

```python
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
