Instructions to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-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 abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-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 abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Use Docker

docker model run hf.co/abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Llama new
LM Studio
Jan

vLLM

How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-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": "abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Ollama
How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M
```
Unsloth Desktop

How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_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": "abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M
```

Lemonade

How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-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 abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_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 abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_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 "abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ2_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-X2.6-4B-Uncensored GSQ-RCO GGUF

Mixed-precision GSQ-RCO GGUF quantizations of XHToken/Spark-X2.6-4B, optimized for efficient, high-fidelity local inference in llama.cpp. The model supports a context length of up to 1,048,576 tokens (1M).

Benchmark

Why GSQ-RCO?

Standard quantization severely degrades hybrid-attention architectures (e.g. stock low-bit quantizations cause representation drift and degradation in reasoning coherence).

GSQ-RCO solves this through two targeted techniques:

RCO: Keeps all 36 sigmoid headwise attention gates (g_proj) and the 9 global full-attention projections in unquantized BF16.
GSQ: Optimizes per-layer precision guided by Hessian sensitivity calibration.

GGUF Files

File	bpw	Size
Spark-X2.6-4B-GSQ-RCO-IQ2_M.gguf	2.60	1.64 GB
Spark-X2.6-4B-GSQ-RCO-IQ3_S.gguf	3.25	2.05 GB
Spark-X2.6-4B-GSQ-RCO-IQ3_M.gguf (Recommended)	3.75	2.28 GB
Spark-X2.6-4B-GSQ-RCO-IQ4_XS.gguf	4.25	2.59 GB

Usage

llama.cpp

Interactive Chat

llama-cli \
  -m Spark-X2.6-4B-GSQ-RCO-IQ3_M.gguf \
  -ngl 99 \
  -fa \
  -b 2048 \
  -c 16384 \
  -cnv

Local Server (OpenAI-Compatible)

llama-server \
  -m Spark-X2.6-4B-GSQ-RCO-IQ3_M.gguf \
  -ngl 99 \
  -fa \
  -b 2048 \
  -c 32768 \
  --port 8080

128k Long-Context (Low-VRAM) Using 4-bit KV cache (-ctk q4_0 -ctv q4_0) allows 131k context to run within ~3.5 – 4 GB total VRAM:

llama-server \
  -m Spark-X2.6-4B-GSQ-RCO-IQ3_M.gguf \
  -ngl 99 \
  -fa \
  -b 2048 \
  -c 131072 \
  -ctk q4_0 -ctv q4_0 \
  --port 8080

Ollama

ollama run hf.co/abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF:IQ3_M

Attribution & License

Distributed under the Apache-2.0 License, matching upstream XHToken/Spark-X2.6-4B.
Base model: XHToken/Spark-X2.6-4B.

Downloads last month: 5,500

GGUF

Model size

4B params

Architecture

spark2_5

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for abenzerps/Spark-X2.6-4B-Uncensored-GSQ-RCO-GGUF

Base model

XHToken/Spark-X2.6-4B-Base

Finetuned

XHToken/Spark-X2.6-4B

Quantized

(54)

this model

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