Instructions to use XHToken/Spark-X2.5-1.7B-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 XHToken/Spark-X2.5-1.7B-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 XHToken/Spark-X2.5-1.7B-GGUF
# Run inference directly in the terminal:
llama cli -hf XHToken/Spark-X2.5-1.7B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XHToken/Spark-X2.5-1.7B-GGUF
# Run inference directly in the terminal:
llama cli -hf XHToken/Spark-X2.5-1.7B-GGUF

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 XHToken/Spark-X2.5-1.7B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf XHToken/Spark-X2.5-1.7B-GGUF

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 XHToken/Spark-X2.5-1.7B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XHToken/Spark-X2.5-1.7B-GGUF

Use Docker

docker model run hf.co/XHToken/Spark-X2.5-1.7B-GGUF

LM Studio
Jan

vLLM

How to use XHToken/Spark-X2.5-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XHToken/Spark-X2.5-1.7B-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": "XHToken/Spark-X2.5-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XHToken/Spark-X2.5-1.7B-GGUF

Ollama
How to use XHToken/Spark-X2.5-1.7B-GGUF with Ollama:
```
ollama run hf.co/XHToken/Spark-X2.5-1.7B-GGUF
```
Unsloth Desktop

How to use XHToken/Spark-X2.5-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XHToken/Spark-X2.5-1.7B-GGUF

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": "XHToken/Spark-X2.5-1.7B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use XHToken/Spark-X2.5-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/XHToken/Spark-X2.5-1.7B-GGUF
```

Lemonade

How to use XHToken/Spark-X2.5-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XHToken/Spark-X2.5-1.7B-GGUF

Run and chat with the model

lemonade run user.Spark-X2.5-1.7B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use XHToken/Spark-X2.5-1.7B-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 XHToken/Spark-X2.5-1.7B-GGUF

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 XHToken/Spark-X2.5-1.7B-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use XHToken/Spark-X2.5-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XHToken/Spark-X2.5-1.7B-GGUF

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 "XHToken/Spark-X2.5-1.7B-GGUF" \
  --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-X2.5-1.7B-GGUF

This repository provides a BF16 GGUF conversion of Spark-X2.5-1.7B.

Spark-X2.5 is a compact, general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. It uses a hybrid attention architecture, supports a native context length of up to 1M tokens, and covers more than 200 languages. For its architecture, training methods, benchmark results, fine-tuning, and citation, see the Spark-X2.5-1.7B.

Local Deployment

The GGUF file can be used for local inference with Ollama and LM Studio. Spark-X2.5 support is provided by XHToken/llama.cpp, so the Quick Starts below use this compatible implementation.

Ollama Quick Start

Build

git clone https://github.com/XHToken/llama.cpp.git llama.cpp-spark
git clone https://github.com/ollama/ollama.git ollama-spark
cd ollama-spark
export OLLAMA_LLAMA_CPP_SOURCE="$(cd ../llama.cpp-spark && pwd)"
cmake -S . -B build
cmake --build build --parallel 8

Import the GGUF

Replace the model path below with the absolute path to the downloaded GGUF file:

printf 'FROM /absolute/path/to/Spark-X2.5-1.7B.gguf\n' > ./Modelfile.spark

Create and Run

Start the Ollama server in the first terminal:

./ollama serve

Open a second terminal in the same ollama-spark directory:

./ollama create Spark-X2.5-1.7B -f ./Modelfile.spark
./ollama run Spark-X2.5-1.7B --think=false

--think=false disables thinking mode for faster, direct responses.

LM Studio Quick Start

Build the Compatible llama.cpp Runtime

git clone https://github.com/XHToken/llama.cpp.git llama.cpp-spark
cd llama.cpp-spark
cmake -S . -B build
cmake --build build --parallel 8

Configure LM Studio

Close LM Studio.

Back up the selected LM Studio runtime directory:

<LM_STUDIO_HOME>/extensions/backends/<selected-runtime>/

Copy the llama.cpp-spark build output into the selected runtime directory, replacing the existing runtime files.
Place Spark-X2.5-1.7B.gguf in:
```
<LM_STUDIO_HOME>/models/<org>/<name>/
```

Example runtime directory on Apple Silicon:

./build/bin/* -> ~/.lmstudio/extensions/backends/llama.cpp-mac-arm64-apple-metal-advsimd-<version>/

Run

Open LM Studio, select the model under My Models, click Load, and start a new chat.

You can also use the lms CLI:

lms ls
lms load <model>
lms chat <model>

License

Released under the Apache License 2.0.

Downloads last month: 26

GGUF

Model size

2B params

Architecture

spark2_5

Hardware compatibility

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Model tree for XHToken/Spark-X2.5-1.7B-GGUF

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Collection including XHToken/Spark-X2.5-1.7B-GGUF

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