Instructions to use shuttleai/shuttle-3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shuttleai/shuttle-3.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shuttleai/shuttle-3.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shuttleai/shuttle-3.5")
model = AutoModelForCausalLM.from_pretrained("shuttleai/shuttle-3.5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shuttleai/shuttle-3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shuttleai/shuttle-3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuttleai/shuttle-3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shuttleai/shuttle-3.5

SGLang

How to use shuttleai/shuttle-3.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shuttleai/shuttle-3.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuttleai/shuttle-3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shuttleai/shuttle-3.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shuttleai/shuttle-3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shuttleai/shuttle-3.5 with Docker Model Runner:
```
docker model run hf.co/shuttleai/shuttle-3.5
```

shuttle-3.5 / README.md

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	---
	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
	---


	<p style="font-size:20px;" align="left">
	<div style="border-radius: 15px;">
	<img
	src="https://storage.shuttleai.com/shuttle-3.5.png"
	alt="ShuttleAI Thumbnail"
	style="width: auto; height: auto; margin-left: 0; object-fit: cover; border-radius: 15px;">
	</div>

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