---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/shuttleai/shuttle-3.5/blob/main/LICENSE
pipeline_tag: text-generation
language:
- en
tags:
- chat
---


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## Shuttle-3.5
### ☁️ <a href="https://shuttleai.com/" target="_blank">Use via API</a> • 💬 <a href="https://shuttlechat.com/" target="_blank">ShuttleChat</a> 

We are excited to introduce Shuttle-3.5, a fine-tuned version of [Qwen3 32b](https://huggingface.co/Qwen/Qwen3-32B), emulating the writing style of Claude 3 models and thoroughly trained on role-playing data.

- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.


## Model Overview

**Shuttle 3.5** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 32.8B
- Number of Paramaters (Non-Embedding): 31.2B
- Number of Layers: 64
- Number of Attention Heads (GQA): 64 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). 


## Fine-Tuning Details

- **Training Setup**: The model was trained on 130 million tokens for 40 hours on an H100 GPU.