Instructions to use ngquocvinh/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 ngquocvinh/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 ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/Spark-X2.5-1.7B-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 ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngquocvinh/Spark-X2.5-1.7B-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 ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ngquocvinh/Spark-X2.5-1.7B-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 ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ngquocvinh/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 "ngquocvinh/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": "ngquocvinh/Spark-X2.5-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M

Ollama
How to use ngquocvinh/Spark-X2.5-1.7B-GGUF with Ollama:
```
ollama run hf.co/ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ngquocvinh/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 ngquocvinh/Spark-X2.5-1.7B-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": "ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ngquocvinh/Spark-X2.5-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Spark-X2.5-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ngquocvinh/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 ngquocvinh/Spark-X2.5-1.7B-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 ngquocvinh/Spark-X2.5-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Community GGUF quantizations of XHToken/Spark-X2.5-1.7B. This repository contains nine quantized files for local inference. No training or fine-tuning was performed.

☕ If you find this GGUF useful, please consider
buying me a coffee.
Your support helps cover the GPU costs of future releases.
Thank you for supporting this work.

Files

Quantization	File size (GiB)	A10M generation token/s	Validation	Recommendation / Notes
Q8_0	1.70	175.54	Load/generate pass	High quality.
Q6_K	1.31	199.07	Load/generate pass	High quality.
Q5_K_M	1.17	223.04	Load/generate pass	Daily use.
Q4_K_M	1.03	241.87	Load/generate pass	Recommended default.
Q3_K_M	0.87	203.60	Load/generate pass	Lower-memory profile.
Q2_K	0.74	231.70	Load/generate pass	Aggressive low-memory profile.
IQ2_XS	0.61	245.03	Load/generate pass	Experimental.
IQ1_M	0.54	252.02	Load/generate pass	Experimental.
Q1_0	0.40	336.65	Load/generate pass	Experimental / legacy minimum-memory option.

The A10M generation figures were measured with single-stream llama-bench on an NVIDIA A10M.

Q1/Q2 and the IQ variants can lose instruction following, reasoning, and tool-call reliability. Validate the chosen file on the workload that matters to you.

License and attribution

The upstream model is released under Apache License 2.0. Preserve the upstream attribution and license when redistributing these derivative files. This is a community GGUF quantization, not an official XHToken/SparkLLM release or endorsement.

Checksums are available in SHA256SUMS.txt.

Downloads last month: 1,169

GGUF

Model size

2B params

Architecture

spark2_5

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +3 variants

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

Base model

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

Finetuned

XHToken/Spark-X2.5-1.7B

Quantized

(9)

this model