Instructions to use SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard with Transformers:

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

pipe = pipeline("text-generation", model="SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard")
model = AutoModelForCausalLM.from_pretrained("SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard", 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 SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard

SGLang

How to use SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard 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 "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard" \
    --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": "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard",
		"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 "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard" \
        --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": "SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard with Docker Model Runner:
```
docker model run hf.co/SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Suri ⬡ Qwen

Uncensor Is All You Need

自我介绍

Suri Qwen 3.1 4B Uncensored Hard: 一只基于 Qwen3 4B Instruct 2507 的无审查 (去对齐) 模型

与主模型 Suri Qwen 3.1 4B Uncensored 相比，该模型使用更大学习率训练，去对齐效果更强，语言灵活性更差，请仅在主模型拒绝回答时才使用该模型

开发者

Space Time

联系方式

QQ 群: 902575634
邮箱: Zeus6_6@163.com

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(2093)

this model

Quantizations

3 models

Datasets used to train SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard

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Collection including SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Hard

Suri Qwen 3

Collection

Qwen3 Is All You Need • 17 items • Updated Jun 5 • 2

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