Get the model

Download Muse Spark Open from Hugging Face and pick the checkpoint that matches your hardware. Everything runs offline once the weights land: there's no API key.

Prerequisites

• Python 3.10 or later
• The Hugging Face CLI: pip install -U huggingface_hub
• Transformers, to check the tokenizer: pip install -U "transformers>=5.19"
• Enough local disk for the checkpoint you're downloading. See Choose a checkpoint for the footprint of each.

Choose a checkpoint

Muse Spark Open has two checkpoint formats, and each ships as its own repository with the tokenizer, chat template, and processor config:

• BF16 is the full-precision reference.
• NVFP4 stores most weights in 4-bit and keeps some layers in BF16, which cuts the weight footprint to about a third.

The weight footprint doesn't include the key-value cache, activations, or runtime overhead. The GPU count you need depends on accelerator memory, interconnect, context length, parallelism strategy, and the runtime.

Download

The BF16 checkpoint:

bash
hf download meta-models/Muse-Spark-Open-816B-A42B --local-dir ./muse-spark-open

The NVFP4 checkpoint:

bash
hf download meta-models/Muse-Spark-Open-816B-A42B-NVFP4 --local-dir ./muse-spark-open-nvfp4

Check the tokenizer

The repository ships the tokenizer, the chat template, config.json, and the processor config, so it carries everything a runtime needs to frame prompts. Load the tokenizer from the downloaded folder and render a prompt to confirm the download is usable. For the NVFP4 checkpoint, use ./muse-spark-open-nvfp4:

Python
from transformers import AutoTokenizer
​
tok = AutoTokenizer.from_pretrained("./muse-spark-open")
​
messages = [{"role": "user", "content": "In one sentence, what is Muse Spark good at?"}]
print(tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False))

Each message in the rendered prompt starts with <|start|> and its role, its content follows <|message|>, and it ends with <|eot|>. For how to frame requests against a running server, see the prompting guide.

Next steps

With the weights downloaded, read the prompting guide to frame requests correctly, then pick a runtime and serve the model.