Instructions to use PinkPixel/Snarkle-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PinkPixel/Snarkle-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PinkPixel/Snarkle-2B-GGUF",
	filename="snarkle2b.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "\"cats.jpg\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PinkPixel/Snarkle-2B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/PinkPixel/Snarkle-2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use PinkPixel/Snarkle-2B-GGUF with Ollama:
```
ollama run hf.co/PinkPixel/Snarkle-2B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use PinkPixel/Snarkle-2B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PinkPixel/Snarkle-2B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PinkPixel/Snarkle-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PinkPixel/Snarkle-2B-GGUF to start chatting

Pi new

How to use PinkPixel/Snarkle-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Snarkle-2B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PinkPixel/Snarkle-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/PinkPixel/Snarkle-2B-GGUF:Q4_K_M
```

Lemonade

How to use PinkPixel/Snarkle-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PinkPixel/Snarkle-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Snarkle-2B-GGUF-Q4_K_M

List all available models

lemonade list

✨ Snarkle 2B GGUF ✨

This repository contains the GGUF versions of PinkPixel/Snarkle-2B. These files are compatible with llama.cpp and various other local LLM runners like LM Studio, Jan, and AnythingLLM.

😼 About Snarkle 2B

Snarkle 2B is a sarcastically-tuned vision model based on Qwen3.5-2B. It has been trained extensively to provide witty, sarcastic, and humorous responses by default, while maintaining its ability to analyze images and provide helpful information when pushed.

🎭 Personality Examples

User: "Hello, how are you today?"
Snarkle: "I'm a collection of weights and biases stored on a server. The only question I've been asked today is how to get a hold of you, and I don't even have a smiley face function."

User: "Tell me a joke."
Snarkle:

"I'm in line behind a lady with 10,000 coupons. I have a full belly and I'm starting to lose my shit."

📦 Quantization Sizes

We provide several quantization levels to balance performance and memory usage:

File Name	Size	Description
`snarkle2b.F16.gguf`	3.78 GB	Full 16-bit precision. Best quality.
`snarkle2b.Q8_0.gguf`	2.01 GB	8-bit quantization. Near-perfect quality.
`snarkle2b.Q6_K.gguf`	1.56 GB	6-bit quantization. Great balance.
`snarkle2b.Q5_K_M.gguf`	1.41 GB	5-bit quantization. Recommended for most users.
`snarkle2b.Q4_K_M.gguf`	1.27 GB	4-bit quantization. High efficiency.
`snarkle2b.Q3_K_M.gguf`	1.10 GB	3-bit quantization. Very small, slightly lower quality.
`snarkle2b.Q2_K_L.gguf`	1.09 GB	2-bit quantization. Smallest possible size.
`snarkle2b.BF16-mmproj.gguf`	671 MB	Multimodal projector file (required for vision).

Note: For vision support, you typically need to load both the model GGUF and the mmproj GGUF in your runner.

🛠️ Usage with llama.cpp

Note: Because Qwen3.5 architecture is still very new, vision capabilities may not yet be compatible.

./llama-cli -m snarkle2b.Q5_K_M.gguf --mmproj snarkle2b.BF16-mmproj.gguf -p "Describe this image sarcastically." --image your_image.png

⚖️ License

This model is released under the Apache 2.0 license.

Made with ❤️ by Pink Pixel ✨

Downloads last month: 380

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for PinkPixel/Snarkle-2B-GGUF

Base model

Qwen/Qwen2-VL-2B

Finetuned

Qwen/Qwen2-VL-2B-Instruct

Finetuned

PinkPixel/Snarkle-2B

Quantized

(1)

this model