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)

# pip install -U transformers accelerate
# 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=256)
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
```

QuillBytes commited on Sep 20

Commit

fc26a28

verified ·

1 Parent(s): 6937c13

Update README.md

Browse files

Files changed (1) hide show

README.md +98 -13

README.md CHANGED Viewed

@@ -1,21 +1,106 @@
 ---
 tags:
-- gguf
-- llama.cpp
 - unsloth
-- vision-language-model
 ---
-# surfer : GGUF
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf QuillBytes/surfer --jinja`
-- For multimodal models: `llama-mtmd-cli -hf QuillBytes/surfer --jinja`
-## Available Model files:
-- `Qwen3.5-4B.Q4_K_M.gguf`
-- `Qwen3.5-4B.F16-mmproj.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+license: apache-2.0
+language:
+- en
 tags:
+- surfer
+- surfing
+- character
+- fine-tuned
 - unsloth
+- lora
+- gguf
+- qwen3
+- fun
+library_name: transformers
+pipeline_tag: text-generation
 ---
+# 🏄 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
+1. Download the .gguf file from this repo
+2. Open LM Studio
+3. Drag and drop the .gguf file in
+4. Set the system prompt to the surfer one above
+5. 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! 🏄🌊🤙*