Transformers documentation

Muse Spark Open

Transformers

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Model ini disumbangkan ke Hugging Face Transformers pada 2026-10-14.

Muse Spark Open

Ikhtisar

Muse Spark Open adalah model multimodal mixture-of-experts (MoE) yang dikembangkan oleh Meta, menerima teks, gambar, audio, dan video sebagai input, serta menghasilkan teks. Model ini memiliki total 816 miliar parameter, dengan 42 miliar parameter aktif per token, dan mendukung jendela konteks hingga 1.048.576 token yang dibagikan di antara semua modalitas input. Model ini dilatih untuk tugas-tugas coding agentic, termasuk pengeditan kode multi-file, debugging, dan penggunaan alat.

Tulang punggung bahasa terdiri dari 62 lapisan transformer, di mana setiap lapisan merutekan token ke 8 dari 256 ahli. Model ini menggunakan grouped-query attention dengan normalisasi query-key, gating dan normalisasi output perhatian, serta gerbang residual yang dipelajari. Encoder persepsi bersama memproses gambar dan bingkai video, sementara encoder audio konvolusional menghasilkan representasi audio pada 12,5 token per detik. Muse Spark Open berbagi tokenizer dan kosakata dengan Muse Glimmer, dengan ekstensi pada template chat untuk input multimodal. Checkpoint tersedia dalam format BF16 dan NVFP4.

Sumber daya

AutoModel

AutoModel Image

AutoModel Video

AutoModel Audio

import os

from transformers import AutoModelForMultimodalLM, AutoProcessor, DistributedConfig

distributed_config = DistributedConfig(
    tp_size=int(os.environ["WORLD_SIZE"]), # Number of devices across which the dense layers are sharded
    ep_size=int(os.environ["WORLD_SIZE"]), # Number of devices across which the experts are distributed
)

processor = AutoProcessor.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 = processor.apply_chat_template(
    messages,
    reasoning_effort="low",  # default: xhigh, options: low/medium/high/xhigh
    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=512)
print(processor.decode(outputs[0][input_len:], skip_special_tokens=False))
#  to=self<|message|>Explain in one sentence why the sky is usually blue.
# ...
# <|eom|><|start|>assistant to=user<|message|>The sky is usually blue because molecules in Earth's atmosphere scatter shorter-wavelength blue sunlight in all directions much more strongly than longer-wavelength red light.<|eot|>

import os

from transformers import AutoModelForMultimodalLM, AutoProcessor, DistributedConfig

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

processor = AutoProcessor.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": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]
inputs = processor.apply_chat_template(
    messages,
    reasoning_effort="medium", # default: xhigh, options: low/medium/high/xhigh
    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=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
print(response)
#  to=self<|message|>What is shown in this image?
# ...
# <|eom|><|start|>assistant to=user<|message|>The image shows a **Pallas's cat — also known as a manul, *Otocolobus manul*** — walking through snow.
#
# It is a small, stocky wild cat recognizable by:
#
# * **Its extremely dense, long, greyish-brown winter coat** with faint dark stripes and a dusting of snow on its back
# * **Its flat, broad face** with low-set ears, a short muzzle, and dark stripes on the cheeks
# * **Its short legs and thick, plump body** adapted for cold climates
#
# The cat is captured mid-stride on a snow-covered surface, with one front paw lifted. In the background are white birch tree trunks with black markings and a chain-link fence on the left, indicating the animal is in a zoo or wildlife enclosure in winter.<|eot|>

import os

from transformers import AutoModelForMultimodalLM, AutoProcessor, DistributedConfig

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

processor = AutoProcessor.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": [
            {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
            {"type": "text", "text": "What is happening in this video?"},
        ],
    },
]
inputs = processor.apply_chat_template(
    messages,
    reasoning_effort="medium",  # default: xhigh, options: low/medium/high/xhigh
    processor_kwargs={
        "fps": 2,   # default: 2, number of frames sampled per second
        "num_frames": 8, # default: 94, maximum number of frames sampled from the video
    },
    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=512)
print(processor.decode(outputs[0][input_len:], skip_special_tokens=False))
#  to=self<|message|>What is happening in this video?
# ...
# <|eom|><|start|>assistant to=user<|message|>In this video two people are playing tennis on an indoor court.
#
# The shot is taken from behind one of the players - a man in a white t-shirt, black shorts and white shoes and socks. He is standing near the baseline on an orange hard court with white lines, in a ready position with his knees bent and his racket held in front of him.
#
# Across the net, on the far side of the court, is his opponent, also dressed in light-colored clothing.
#
# The sequence shows a short rally:
#
# *   The opponent hits the ball over the net.
# *   The man in the foreground shuffles to his left, turns his body, and prepares for a shot.
# *   He executes a two-handed backhand, swinging the racket across his body and following through as he hits the ball back over the net.
#
# The setting is a large indoor tennis hall with a high, arched ceiling with exposed wooden beams and overhead fluorescent lights. The court is surrounded by a grey run-off area, green windscreening on the walls, and netting that separates it from adjacent courts. On the right side you can see an umpire's chair and a few yellow and black benches. An "Adobe Stock" watermark is visible in the center of the frame.<|eot|>

Bobot NVFP4

Bobot terkuantisasi NVFP4 quantized weights dengan persyaratan memori yang lebih rendah tersedia di hub. Model dapat dimuat pada perangkat yang kompatibel dengan NVFP4 tanpa perubahan pada kode. Konfigurasi kuantisasi diterapkan secara otomatis saat pemuatan.

Untuk dukungan NVFP4, transformers harus diinstal dari cabang ini.
pip install git+https://github.com/huggingface/transformers.git@finegrained-per-scheme-demo

import os

from transformers import AutoModelForMultimodalLM, AutoProcessor, DistributedConfig

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

processor = AutoProcessor.from_pretrained("meta-models/Muse-Spark-Open-816B-A42B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained(
    "meta-models/Muse-Spark-Open-816B-A42B-NVFP4",
    distributed_config=distributed_config,
)

# ...

Transformers

Muse Spark Open

Ikhtisar

Sumber daya

Bobot NVFP4

MuseSparkConfig

kelas transformers.MuseSparkConfig

MuseSparkTextConfig

kelas transformers.MuseSparkTextConfig

MuseSparkVisionConfig

kelas transformers.MuseSparkVisionConfig

MuseSparkAudioConfig

kelas transformers.MuseSparkAudioConfig

MuseSparkFeatureExtractor

kelas transformers.MuseSparkFeatureExtractor

MuseSparkProcessor

kelas transformers.MuseSparkProcessor

MuseSparkPreTrainedModel

kelas transformers.MuseSparkPreTrainedModel

MuseSparkTextModel

kelas transformers.MuseSparkTextModel

forward

MuseSparkVisionModel

kelas transformers.MuseSparkVisionModel

forward

MuseSparkAudioModel

kelas transformers.MuseSparkAudioModel

forward

MuseSparkModel

kelas transformers.MuseSparkModel

forward

MuseSparkForConditionalGeneration

kelas transformers.MuseSparkForConditionalGeneration

forward