Muse Spark Open is an open-weight model from Meta. The mixture of experts model brings text, images, audio, and video into a model with approximately 816 billion parameters, around 42 billion active per token, and a context window of up to one million tokens. It generates text, giving developers a common interface for asking questions about different kinds of input.

Following Muse Glimmer, Muse Spark Open introduces a mixture-of-experts architecture at a much larger scale, and the most capable US open-weights model to date.

Meta trained Muse Spark Open for agentic coding: reading a codebase, writing and debugging code, and calling tools. It also takes images, audio, and video as input, so the same model can read a screenshot, listen to a voice note, or watch a screen recording while it works on your code. This post shows you how to run it, how to fine-tune it, and what we built with it.

Overall Capabilities and Architecture

Meta describes Muse Spark Open as a coding-optimized model trained on real developer workflows such as multi-file refactors and long debugging sessions. Muse Spark Open is an agentic focused model that plans, acts, observes, and self-corrects over long tasks, including browsing the web with images.

The checkpoint repositories are Muse-Spark-Open-816B-A42B and Muse-Spark-Open-816B-A42B-NVFP4.

Muse Spark Open is the full-size model of the Muse series of models. Muse Glimmer, which Meta released in August, is a small dense model from the same series, ideal for running locally. Both models use the same tokenizer and vocabulary. The chat template follows the Muse Glimmer template, with additions for the new modalities. In Muse Spark Open the input embedding and the output layer are separate matrices of about 1.66B parameters each.

Mixture of Experts

The language model has 62 layers and a hidden dimension of 8192. Each layer has 128 query heads and 16 key-value heads (grouped-query attention). Each head has 64 dimensions. The attention block has an output gate and an output normalization layer. The model also applies normalization to the queries and the keys (QK-norm). Each block combines its input and its output through a learned residual gate, not through a plain sum.

Muse Glimmer uses one dense feed-forward network in each layer. Muse Spark Open replaces this dense network with a Mixture-of-Experts (MoE) block. Each MoE block has 256 experts, and a router selects 8 experts for each token. Each expert is a feed-forward network that projects from 8192 to 4096 dimensions and back to 8192, then applies its own normalization to its output. The experts hold ~97% of the total model parameters.

Only 8 experts run per token, so the model computes each token with about 42B parameters. All 256 experts stay in memory, so deployment needs the memory of an 816B-parameter model, but runs at the much faster speed of a 42B model. The BF16 checkpoint is about 1.5 TB in safetensors format. The NVFP4 checkpoint stores the parameters in 4-bit precision with negligible loss in accuracy, specifically for NVIDIA Blackwell GPUs. In transformers, the BF16 checkpoint runs across 4 nodes of 8 H200 GPUs with tensor parallelism (TP=32) and the NVFP4 runs on a single B200 node. If you want to deploy Muse Spark Open, check out the llama.cpp, vLLM, and SGLang recipes below.

Perception Encoder

A perception encoder converts images and video frames into tokens for the language model, just like Muse Glimmer. The encoder has 1.8B parameters, 50 layers, a hidden dimension of 1536, and 16 attention heads. It splits each image into 14×14 patches and produces features with 4096 dimensions. Position information comes from learned absolute embeddings and 2D RoPE. The feed-forward network in each layer uses LayerNorm and GELU. Every fourth layer and the last layer use full attention, and the other layers use sparse attention.

Muse Glimmer 30B uses an encoder with the same architecture. The design follows the Perception Encoder work from Meta. Video input goes through the same encoder as images.

Audio Encoder

Muse Spark Open accepts audio as input through its audio encoder. An audio tokenizer converts the audio into tokens, and the chat template marks the audio position with <|audio|> tokens. The encoder emits one audio token every 80 ms, which yields 12.5 tokens per second. So let’s say, you have an audio of 10 seconds, the number of input audio tokens would be 125. The encoder itself is a convolutional pyramid where each stage consists of a patch embedder of different patch sizes and 2 convolutional blocks of 1d kernels of size 7 with stride of 1. These stages are repeated for patch sizes of [16, 6, 5, 4]. At the end of the final stage, the temporal frames are fed to a final block of RMSNorm → linear projection → RMSNorm.

Long Context

The context window is 1M tokens at inference. The configuration sets max_position_embeddings to 1,048,576. This includes image, video, and audio tokens in the same window as text.

Grouped-query attention keeps the KV cache small for a model of this size. Each token adds 2 × 16 heads × 64 dimensions × 62 layers = 126,976 values to the cache. In BF16, this is about 250 KB per token and about 266 GB for a full 1M-token context.

Inference Support

Transformers

The Muse Spark Open implementation provides MuseSparkForConditionalGeneration for text generation from multimodal inputs and MuseSparkProcessor for preparing conversations. The processor combines the tokenizer, image and video processors, audio feature extractor, and chat template.

Use a DistributedConfig to configure the parallelism settings. tp_size is the number of devices across which the dense layers are sharded, and ep_size is the number of devices across which the experts are distributed.

You can initialize the model and processor as follows.

import os

from transformers import AutoModelForMultimodalLM, AutoTokenizer, DistributedConfig

distributed_config = DistributedConfig(
    tp_size=int(os.environ["WORLD_SIZE"]),
    ep_size=int(os.environ["WORLD_SIZE"]),
)

tokenizer = AutoTokenizer.from_pretrained("meta-models/Muse-Spark-Open-816B-A42B
")
model = AutoModelForMultimodalLM.from_pretrained(
    "meta-models/Muse-Spark-Open-816B-A42B",
    distributed_config=distributed_config,
)

messages = [
    {"role": "user", "content": "Explain in one sentence why the sky is usually blue."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    reasoning_effort="medium",  
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device).to(model.dtype)

input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=2000)

Text-only Inference

Prepare the conversation, then pass the messages to processor.apply_chat_template, enabling add_generation_prompt=True, tokenize=True, return_dict=True, and return_tensors="pt". Pass the processor output to model.generate and decode the newly generated tokens, excluding the input prefix. The processor has a lever for the reasoning effort, mapping symbolic levels (low/medium/high/xhigh) to the integers the model itself accepts (16, 64, 256, 512). Note that turning off reasoning is not possible with Muse Spark Open, so keep the model reasoning at “low” if you want to consume less tokens to solve an easier task. You can also set a numeric effort by including a line like Reasoning strength: 1 in the system prompt.

messages = [
    {"role": "system", "content": "You should only answer with a number."},
    {"role": "user", "content": "What is 17 * 23?"},
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    reasoning_effort="high", 
).to(model.device).to(model.dtype)

output = model.generate(**inputs, max_new_tokens=2000)
generated_tokens = output[0][inputs["input_ids"].shape[1] :]
print(processor.decode(generated_tokens, skip_special_tokens=False))

Images and Text

The Muse Spark Open processor expands each image marker into the number of patch tokens required by the processed image, surrounded by image boundary tokens. This expansion keeps the text positions aligned with the visual features passed to the model. The processor also handles multiple images within a single prompt.

image_url = (
    "https://huggingface.co/datasets/merve/vl-test-suite/"
    "resolve/main/pills.jpg"
)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_url,
            },
            {
                "type": "text",
                "text": "Do any of the components in this supplement interact?",
            },
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    reasoning_effort="medium",
    return_dict=True,
    return_tensors="pt",
).to(model.device).to(model.dtype)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=2000)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

processor.parse_response(response)

Video Inference

Video prompts include frame timing as well as visual content. The processor handles videos with audios in them (which is an upgrade from Muse Glimmer thanks to the audio encoder). The processor downsamples video frames and audio to align frames together. By default, videos are sampled to 2 FPS, capped at 96 frames. You can modify this by setting the fps of the video processor:

processor.video_processor.fps = 4.0

As a general rule of thumb, videos are frames in temporal sequence, and the more you downsample, the more you will lose frames in between specific events, so you need to keep fps high if you want more accurate generations. However, there’s a trade-off because you will require more memory and increase latency for prefill. The frames are interleaved with text input that states timestamps (e.g. “Time: 3.5s”). To enable passing in videos with audio, pass load_audio_from_video=True to apply_chat_template.

Preserve the video's metadata when preparing inputs with return_metadata=True so the processor can assign the correct framerate.

import torch

messages = [
      {
          "role": "user",
          "content": [
{
                  "type": "text",
                  "text": (
                      "Describe what happens in this video. "
                      "Use timestamps and include relevant speech or sounds."
                  ),
              }
              {"type": "video", "url": "https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/ramen.mp4"},
              ,
          ],
      }
  ]

  inputs = processor.apply_chat_template(
      messages,
      add_generation_prompt=True,
      tokenize=True,
      return_dict=True,
      return_tensors="pt",
      load_audio_from_video=True,
      return_metadata=True,
  ).to(model.device).to(model.dtype)

  with torch.inference_mode():
      output_ids = model.generate(
          **inputs,
          max_new_tokens=256,
          do_sample=False,
      )

  generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
  response = processor.batch_decode(
      generated_ids,
      skip_special_tokens=True,
  )[0]

  print(response)

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