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

Libraries

How to use sizzlebop/Spark-X2.5-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="sizzlebop/Spark-X2.5-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sizzlebop/Spark-X2.5-4B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sizzlebop/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 sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/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 sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/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 sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sizzlebop/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 sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use sizzlebop/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 "sizzlebop/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": "sizzlebop/Spark-X2.5-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use sizzlebop/Spark-X2.5-4B-GGUF 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 "sizzlebop/Spark-X2.5-4B-GGUF" \
    --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": "sizzlebop/Spark-X2.5-4B-GGUF",
		"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 "sizzlebop/Spark-X2.5-4B-GGUF" \
        --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": "sizzlebop/Spark-X2.5-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

How to use sizzlebop/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 sizzlebop/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": "sizzlebop/Spark-X2.5-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sizzlebop/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 sizzlebop/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 "sizzlebop/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

This repository contains GGUF format quants for XHToken/Spark-X2.5-4B.

Spark-X2.5-4B is a compact, high-efficiency language model featuring a hybrid attention architecture (combining sliding-window attention with full attention layers) and native context lengths up to 1M tokens.

These GGUF files were converted from the original safetensors weights using a custom Spark 2.5 build of llama.cpp at BF16 precision, then quantized into standard k-quants.

Available Files and Quantizations

File	Quant Type	Size	Description / Recommendation
`Spark-X2.5-4B-BF16.gguf`	BF16	7.66 GB	Full precision base conversion. Highest fidelity, largest size.
`Spark-X2.5-4B-Q8_0.gguf`	Q8_0	4.07 GB	Near-lossless 8-bit quantization. Recommended if you have 6GB+ VRAM.
`Spark-X2.5-4B-Q6_K.gguf`	Q6_K	3.15 GB	Excellent quality retention with minimal loss. Very balanced choice.
`Spark-X2.5-4B-Q5_K_M.gguf`	Q5_K_M	2.77 GB	Good balance between memory footprint and output quality.
`Spark-X2.5-4B-Q4_K_M.gguf`	Q4_K_M	2.42 GB	Fast, lightweight 4-bit quant. Great default for laptops and mobile devices.
`Spark-X2.5-4B-Q3_K_M.gguf`	Q3_K_M	2.02 GB	Lower memory footprint when RAM or VRAM is tight.
`Spark-X2.5-4B-Q2_K.gguf`	Q2_K	1.66 GB	Maximum compression. Noticeable quality degradation, best for ultra-constrained environments.

Requirements

Spark-X2.5 uses the Spark2_5ForCausalLM (spark2_5) architecture. To run these files, ensure your local llama.cpp installation or downstream GUI client includes Spark 2.5 runtime support.

How to Use

1. With llama.cpp

Run the model directly from the command line using llama-cli:

llama-cli -m ./Spark-X2.5-4B-Q4_K_M.gguf \
    -p "You are a helpful and concise assistant.\nUser: Hello! What can you do?\nAssistant:" \
    -n 512 \
    -c 4096 \
    --temp 0.7

To run as a local server:

llama-server -m ./Spark-X2.5-4B-Q4_K_M.gguf \
    --host 127.0.0.1 \
    --port 8080 \
    -c 4096

2. With Ollama

Create a Modelfile in the same directory:

FROM ./Spark-X2.5-4B-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.7

Then build and run the model:

ollama create spark-x2.5-4b -f Modelfile
ollama run spark-x2.5-4b

3. With LM Studio

Download or copy any .gguf file to your LM Studio models folder.
Search for Spark-X2.5-4B in your local models tab.
Load the model with default GPU offloading settings and chat.

Original Model Details

Original weights: XHToken/Spark-X2.5-4B
Base Architecture: Spark 2.5 (Spark2_5ForCausalLM)
Context Length: Up to 1,048,576 tokens (1M)
Vocabulary Size: 131,072 tokens
License: Apache 2.0

Attribution & Credits

Original model weights and architecture developed by XHToken.

GGUF conversions produced by Pink Pixel.

Made with 💖 by Pink Pixel

Downloads last month: 1,286

GGUF

Model size

4B params

Architecture

spark2_5

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for sizzlebop/Spark-X2.5-4B-GGUF

Base model

XHToken/Spark-X2.5-4B-Base

Finetuned

XHToken/Spark-X2.5-4B

Quantized

(12)

this model