Instructions to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("brainworkup/Spark-X2.6-4B-oQ8e-fp16") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "brainworkup/Spark-X2.6-4B-oQ8e-fp16"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "brainworkup/Spark-X2.6-4B-oQ8e-fp16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "brainworkup/Spark-X2.6-4B-oQ8e-fp16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "brainworkup/Spark-X2.6-4B-oQ8e-fp16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brainworkup/Spark-X2.6-4B-oQ8e-fp16", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "brainworkup/Spark-X2.6-4B-oQ8e-fp16"
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 brainworkup/Spark-X2.6-4B-oQ8e-fp16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use brainworkup/Spark-X2.6-4B-oQ8e-fp16 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "brainworkup/Spark-X2.6-4B-oQ8e-fp16"
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 "brainworkup/Spark-X2.6-4B-oQ8e-fp16" \ --custom-provider-id mlx-lm \ --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 — MLX oQ8e (FP16 quantization source)
This is an MLX quantization prepared by xunlinkx with the oMLX quantization pipeline from XHToken/Spark-X2.6-4B, the upstream SparkLLM model. This repository links back to the original model and follows its Apache-2.0 license. See the upstream model card and official Spark-X2.6 project for model details, intended use, and limitations.
This repo contains the same model architecture and tokenizer, with quantized weights for local MLX inference. The original checkpoint is BF16; it was converted to FP16 before quantization. The -fp16 suffix records that quantization input format. Quantization used oMLX enhanced calibration (128 samples, 512-token sequence length), group size 64, and affine quantization.
Model details
- Base model: XHToken/Spark-X2.6-4B
- Quantization: 8-bit
- Quantization layout: Uniform 8-bit affine quantization for quantized linear layers; group size 64. Remaining tensor storage follows the MLX model format.
- Architecture:
spark2_5 - License: Apache-2.0, inherited from the upstream model. See the upstream license.
- Intended hardware: Apple Silicon Macs using MLX, including M1 through M6 generations. FP16 quantization source was chosen for broad compatibility across older and newer Apple Silicon.
- Use case: The highest-precision variant in this set, with a larger download and memory footprint than the 4-bit and 5-bit builds.
Loading
Use a recent oMLX installation with Spark spark2_5 architecture support. Select this repository as the model in oMLX. The saved config.json contains the quantization metadata needed to load the weights.
For direct generation with an MLX-LM installation that supports Spark:
mlx_lm.generate \
--model xunlinkx/Spark-X2.6-4B-oQ8e-fp16 \
--prompt "What is the capital of Anhui Province?" \
--max-tokens 256 \
--temp 1.0 \
--top-p 0.95
The upstream model recommends temperature 1.0 and top-p 0.95; consult its card for chat-template and thinking-budget settings supported by your runtime.
Validation
A local short-prompt generation smoke check completed for this quantized checkpoint: 16 generated tokens; peak memory reported by MLX: 4.530 GB. This is a basic load-and-generate check, not a benchmark or a quality comparison. Memory requirements increase with context length and runtime overhead.
Attribution
Original model and research are by the SparkLLM team at XHToken. This repository contains a community quantization by xunlinkx; it is not an official XHToken release and has not been endorsed by the upstream authors.
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