Instructions to use Ks01/UwU-Qwen3.5-27B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ks01/UwU-Qwen3.5-27B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Ks01/UwU-Qwen3.5-27B-v0.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Ks01/UwU-Qwen3.5-27B-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("Ks01/UwU-Qwen3.5-27B-v0.1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ks01/UwU-Qwen3.5-27B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ks01/UwU-Qwen3.5-27B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ks01/UwU-Qwen3.5-27B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ks01/UwU-Qwen3.5-27B-v0.1

SGLang

How to use Ks01/UwU-Qwen3.5-27B-v0.1 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 "Ks01/UwU-Qwen3.5-27B-v0.1" \
    --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": "Ks01/UwU-Qwen3.5-27B-v0.1",
		"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 "Ks01/UwU-Qwen3.5-27B-v0.1" \
        --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": "Ks01/UwU-Qwen3.5-27B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ks01/UwU-Qwen3.5-27B-v0.1 with Docker Model Runner:
```
docker model run hf.co/Ks01/UwU-Qwen3.5-27B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

UwU-Qwen3.5-27B-v0.1

Experimental. Early-stage fine-tune. Expect rough edges.

Qwen3.5-27B fine-tune for creative writing, roleplay, and reasoning. ~20M tokens (~8.5K samples) of curated data. Retains the base model's vision-language (VL) capabilities.

What This Model Is For

Tuned toward prose-style creative output rather than assistant-style responses.

Descriptive, immersive narrative across genres and tones
Character voice and consistency in multi-turn roleplay
Thinking mode shaped for creative reasoning (narrative structure, character motivation, scene pacing) instead of mechanical step-by-step logic
Vision-language input supported (inherited from Qwen3.5 VL architecture)

Training Data

~8.5K samples, mixed:

Creative writing. Single-turn fiction across genres (literary, noir, horror, romance, sci-fi, etc.) and tones (lyrical, gritty, humorous, sensual, etc.). High-quality curated sources.
Multi-turn roleplay. Extended conversations with character cards. Trains context tracking and character consistency across turns, with diverse archetypes and dynamics.
Reasoning. Filtered reasoning data to keep the base model's analytical ability intact and to steer thinking mode toward structured creative thought.

Model Details

Feature	Description
Base Model	Qwen/Qwen3.5-27B
Architecture	Qwen3.5 VL (27B Dense + MTP)
Precision	bf16
Context Length	131,072 tokens

Thinking Mode

Supports Qwen3.5's native thinking mode:

Thinking. Reasons in <think> block before responding. Useful for complex scenes or multi-character interactions.
No-think. Responds immediately with an empty <think> block. Better for fast, fluid exchanges.

Usage

vLLM

vllm serve Ks01/UwU-Qwen3.5-27B-v0.1 \
  --trust-remote-code \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.90

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Ks01/UwU-Qwen3.5-27B-v0.1",
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("Ks01/UwU-Qwen3.5-27B-v0.1")

messages = [
    {"role": "user", "content": "Write a tense reunion scene between two old friends at a rainy bus stop."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Experimental release (v0.1). Quality may be inconsistent.
Context consistency can degrade in long multi-turn conversations.
Thinking mode occasionally falls into analytical patterns instead of creative reasoning.

Credits

Fine-tuned from ArliAI/Qwen-3.5-27B-Derestricted, based on Qwen/Qwen3.5-27B.

License

Apache 2.0, inherited from Qwen3.5.

Downloads last month: 14

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Ks01/UwU-Qwen3.5-27B-v0.1

Base model

Qwen/Qwen3.5-27B

Finetuned

(312)

this model

Quantizations

2 models

Ks01
/

UwU-Qwen3.5-27B-v0.1

UwU-Qwen3.5-27B-v0.1

What This Model Is For

Training Data

Model Details

Thinking Mode

Usage

vLLM

Transformers

Limitations

Credits

License

Model tree for Ks01/UwU-Qwen3.5-27B-v0.1

Datasets used to train Ks01/UwU-Qwen3.5-27B-v0.1