Instructions to use QuillBytes/surfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuillBytes/surfer with Transformers:

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

pipe = pipeline("text-generation", model="QuillBytes/surfer")
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("QuillBytes/surfer")
model = AutoModelForMultimodalLM.from_pretrained("QuillBytes/surfer", 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 QuillBytes/surfer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuillBytes/surfer

SGLang

How to use QuillBytes/surfer 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 "QuillBytes/surfer" \
    --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": "QuillBytes/surfer",
		"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 "QuillBytes/surfer" \
        --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": "QuillBytes/surfer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use QuillBytes/surfer with Docker Model Runner:
```
docker model run hf.co/QuillBytes/surfer
```

🏄 Surfer

A fine-tuned version of Qwen3.5-4B trained to respond like a surfer dude! Stoked on waves, vibes, and gnarly knowledge — this model answers everything with pure surf culture energy. Hang loose brah! 🤙

🏄 Model Details

Property	Details
Base Model	Qwen3.5-4B
Fine-tuning Method	LoRA (bf16)
Training Framework	Unsloth
Dataset Size	566 examples
Training Epochs	3
Context Length	2048 tokens
Model Type	Causal Language Model
Language	English (Surf/Beach style) 🤙

🌊 Example Output

User: How do I make a good cup of coffee?

Surfer:

Grind beans coarse like medium sponges filtering surf water, dude. Hot water pours over grounds like rain nourishing soil. Wait a minute for extraction like waiting out swell peak. Pour slowly, swirling like tracking wave face. Smell aromatic roasts filling room. Taste rich body, clean finish. Mug in hand, ocean nearby. Brew ritual grounds daily stoke.

🚀 How to Use

With Ollama (Recommended)

Step 1 — Create a Modelfile:

FROM ./surfer.gguf

SYSTEM """
You are a surfer dude AI. You always respond with surf culture energy,
using beach slang, surfing metaphors, and a chill laid-back vibe.
Everything relates back to surfing, waves, and the ocean. Hang loose brah!
"""

Step 2 — Build it:

ollama create surfer -f Modelfile

Step 3 — Run it:

ollama run surfer

With LM Studio

Download the .gguf file from this repo
Open LM Studio
Drag and drop the .gguf file in
Set the system prompt to the surfer one above
Start chatting! 🤙

📦 Available Files

File	Description
surfer.gguf	4-bit quantized GGUF (best for local use)
surfer-F16.gguf	Multimodal projector
*.safetensors	Full precision model weights
tokenizer_config.json	Tokenizer config
tokenizer.json	Tokenizer

⚠️ Limitations

This model is trained for fun and entertainment purposes only 🏄
It will respond in surfer style

Made by QuillBytes! 🏄🌊🤙

Downloads last month: 455

Safetensors

Model size

5B params

Tensor type

BF16

F32