Instructions to use WhiskyAKM/Spark-X2.5-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Spark-X2.5-4B-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 WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/Spark-X2.5-4B-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 WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiskyAKM/Spark-X2.5-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WhiskyAKM/Spark-X2.5-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Ollama
How to use WhiskyAKM/Spark-X2.5-4B-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WhiskyAKM/Spark-X2.5-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M
```

Lemonade

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Spark-X2.5-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WhiskyAKM/Spark-X2.5-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "WhiskyAKM/Spark-X2.5-4B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Spark-X2.5-4B GGUF

GGUF quantized versions of XHToken/Spark-X2.5-4B, a reasoning-capable language model designed for efficient deployment.

Model Overview

Spark-X2.5-4B is a high-performance 4B parameter model specializing in reasoning and text generation. It supports a thinking/reasoning mode where the model generates a chain-of-thought process before providing the final answer, allowing for more accurate and complex problem solving.

The model utilizes a structured conversation format with role-based delimiters (<|System|>, <|User|>, <|Bot|>) and explicitly handles reasoning via <think> blocks.

Model Architecture

Property	Value
Architecture	Transformer
Parameters	4B
Supported Languages	en, zh

Available GGUF Files

File	Quantization	Use Case
`spark-x2.5-4b-BF16.gguf`	BF16/FP16	Full precision
`spark-x2.5-4b-Q4_0.gguf`	Q4_0	Standard 4-bit quantization
`spark-x2.5-4b-Q4_K_S.gguf`	Q4_K_S	Small 4-bit K-quant
`spark-x2.5-4b-Q4_K_M.gguf`	Q4_K_M	Medium 4-bit K-quant (Recommended)
`spark-x2.5-4b-Q5_K_S.gguf`	Q5_K_S	Small 5-bit K-quant
`spark-x2.5-4b-Q5_K_M.gguf`	Q5_K_M	Medium 5-bit K-quant
`spark-x2.5-4b-Q6_K.gguf`	Q6_K	6-bit K-quant
`spark-x2.5-4b-Q8_0.gguf`	Q8_0	8-bit quantization

Usage

llama.cpp CLI

./llama-cli \
  -m spark-x2.5-4b-Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 1.0 --top-p 0.95 --top-k 20

llama-server (OpenAI-compatible API)

./llama-server \
  -m spark-x2.5-4b-Q4_K_M.gguf \
  --host [IP_ADDRESS] --port 8080

Thinking Mode

Thinking mode is integrated into the model's output. The model generates its internal reasoning process within <think> and </think> tags before providing the final answer. This behavior can be controlled via the enable_thinking parameter in the chat template.

Tool Calling

The model supports structured tool calling. Tool definitions are provided in the system prompt, and the model responds using <tool_call> blocks with specific argument tags, which are then processed and returned via tool response tags.

Generation Parameters

Recommended parameters for optimal performance:

Parameter	Value
Temperature	1.0
Top-P	0.95
Top-K	20

Quantization

These GGUF files were created from the source model using llama-quantize from the llama.cpp project.

Acknowledgements

Original model: XHToken/Spark-X2.5-4B
Quantization tool: llama.cpp

License

Apache 2.0 License

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GGUF

Model size

4B params

Architecture

spark2_5

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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