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*This model was contributed to Hugging Face Transformers on 2026-02-09.*

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# Qwen3.5

[Qwen3.5](https://qwen.ai/blog?id=qwen3.5) is Qwen's natively multimodal foundation model family, trained from scratch on interleaved text, image, and video tokens. It uses a 3:1 hybrid attention stack — three Gated DeltaNet (linear attention) layers for every one Gated Attention (full attention) layer — so long context and vision tokens can be served without paying full quadratic cost on every block.

This page covers the dense Qwen3.5 and Qwen3.6 variants (Qwen/Qwen3.5-9B, Qwen/Qwen3.5-27B, Qwen/Qwen3.6-27B). Qwen3.6 checkpoints share the same architecture and `model_type` as Qwen3.5 and are loaded with the same classes. For the sparse mixture-of-experts variants see [Qwen3.5 MoE](./qwen3_5_moe). The text backbone reuses Qwen3-Next's linear-attention decoder with a three-component multimodal RoPE; the vision tower reuses the Qwen3-VL encoder.

You can find all the official Qwen3.5 checkpoints under the [Qwen](https://huggingface.co/Qwen) organization.

## Quickstart

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="Qwen/Qwen3.5-9B",
    device_map="auto",
)
print(pipe("The capital of France is", max_new_tokens=20)[0]["generated_text"])
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoTokenizer, Qwen3_5ForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
model = Qwen3_5ForCausalLM.from_pretrained(
    "Qwen/Qwen3.5-9B",
    device_map="auto",
)

inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=30)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## Usage tips and notes

- Layers are hybrid: [`Qwen3_5TextConfig`]'s `layer_types` is a per-layer list of `"linear_attention"` or `"full_attention"` that encodes the 3:1 Gated DeltaNet / Gated Attention stack. The DeltaNet path (`Qwen3NextGatedDeltaNet`) needs the optional `causal_conv1d` (from [Dao-AILab](https://github.com/Dao-AILab/causal-conv1d)) and `fla` packages for its fast kernels — without them, the model silently falls back to slower and more memory hungry PyTorch ops.
- Multimodal RoPE splits the head dimension into three components (temporal, height, width) via `mrope_section` on the text config. If you replace the rotary module, preserve this split or position encodings for image and video tokens will be misaligned.
- Use [`Qwen3_5ForCausalLM`] for text-only generation with [`Qwen3_5TextConfig`]; use [`Qwen3_5ForConditionalGeneration`] with the full [`Qwen3_5Config`] and a processor ([`~AutoProcessor.from_pretrained`]) to feed interleaved image/video + text via [`~ProcessorMixin.apply_chat_template`].

## Qwen3_5Config

[[autodoc]] Qwen3_5Config

## Qwen3_5TextConfig

[[autodoc]] Qwen3_5TextConfig

## Qwen3_5VisionConfig

[[autodoc]] Qwen3_5VisionConfig

## Qwen3_5Tokenizer

[[autodoc]] Qwen3_5Tokenizer

## Qwen3_5VisionModel

[[autodoc]] Qwen3_5VisionModel
    - forward

## Qwen3_5TextModel

[[autodoc]] Qwen3_5TextModel
    - forward

## Qwen3_5Model

[[autodoc]] Qwen3_5Model
    - forward

## Qwen3_5ForCausalLM

[[autodoc]] Qwen3_5ForCausalLM
    - forward

## Qwen3_5ForConditionalGeneration

[[autodoc]] Qwen3_5ForConditionalGeneration
    - forward
<<<<<<< HEAD

## Qwen3_5ForSequenceClassification

[[autodoc]] Qwen3_5ForSequenceClassification
    - forward

## Qwen3_5TextForSequenceClassification

[[autodoc]] Qwen3_5TextForSequenceClassification
    - forward

## Qwen3_5ForTokenClassification

[[autodoc]] Qwen3_5ForTokenClassification
    - forward

## Qwen3_5Tokenizer

[[autodoc]] Qwen3_5Tokenizer
=======
>>>>>>> 52b4732861 (docs)
