Meta is back with Muse Glimmer: local, agentic, multimodal, and open source!

Great news from the OGs of open source LLMs! Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for local agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it’s ideal deploying locally for privacy, reducing costs, or just hacking around. It’s intended for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups.

To celebrate, we are shipping with Meta day-0 support in transformers, llama.cpp, vLLM, Inference Endpoints, and other libraries. We built a few cool things and explain our findings in this blog.

Check out the demos below for inspiration.

You can find Muse Glimmer on the Hugging Face Hub.

Benchmarks

Benchmark results

Scores are reported as published. Bold indicates the best result among the compared models; ↓ indicates lower is better.

Category	Benchmark	Muse Glimmer-30B High Reasoning	Gemma4-31B Thinking Mode	Qwen3.6-27B Thinking Mode
General Agentic	MCP Atlas	75.5	54.2	62.5
General Agentic	DeepSearch QA	74.6	61.7	71.1
General Agentic	τ³-Banking	23.5	15.1	16.7
General Agentic	WildClawBench	47.6	37.6	43.2
General Agentic	GDPval-AA	953	811	1141
General Agentic	GAIA2	43.3	36.4	40.0
General Agentic	SkillsBench (With Skills)	44.3	32.4	46.6
General Agentic	OSWorld-Verified	65.9	58.5	75.6
Agentic Coding	SWE-Bench Pro	51.2	36.9	50.2
Agentic Coding	SWE-Bench Verified	76.0	66.6	77.2
Agentic Coding	TerminalBench 2.1	51.7	43.4	60.7
Agentic Coding	SciCode	43.6	43.4	39.8
Multimodal	Charxiv Reasoning	78.8	77.7	78.4
Multimodal	ScreenSpot Pro	75.4	75.9	76.1
Multimodal	OmniDocBench v1.5	75.8	72.5	77.8
Multimodal	MMMU Pro	74	73	75
Safety	CI Memories	Violation (↓): 26.4 Coverage: 64.8	Violation (↓): 12.1 Coverage: 53.0	Violation (↓): 53.4 Coverage: 66.9
Safety	Siren AgentDojo	Attack Success Rate (↓): 28.4 Utility: 94.2	Attack Success Rate (↓): 25.6 Utility: 90.8	Attack Success Rate (↓): 40.3 Utility: 92.7
General Capabilities and Reasoning	IFBench	77.0	76.0	70.8
General Capabilities and Reasoning	AIME 2026	94.7	89.2	94.1
General Capabilities and Reasoning	GPQA Diamond	83.5	85.7	84.2
General Capabilities and Reasoning	Humanity’s Last Exam (Text + No Tools)	22.0	23.6	23.1
General Capabilities and Reasoning	AA-LCR	80.0	68.3	73.3
General Capabilities and Reasoning	Beam 128K	65.1	58.2	63.0

Architecture

Muse Glimmer is a dense 30B parameter model consisting of:

• 2B ViT-style encoder for vision (Perception Encoder)
• 28B parameter text decoder

In addition to the main VLM, there’s also a speculative decoding drafter implemented on DFlash. Usage of this module is optional, and it can provide much faster generation in exchange for some memory cost. We found this drafter to be particularly well suited to structured content generation such as coding.

Text Decoder

The language model uses the following architecture components:

• Hybrid attention: Alternating between three sliding window layers (of 2,048 tokens) using rotary position embedding, followed by a fourth layer that uses full attention and NoPE (no positional embedding). The pattern is therefore (SWA, SWA, SWA, Full), repeated 13 times to a total of 52 layers. This allows the model to retain relative order and distance information with RoPE and preserve information globally with NoPE.
• Gated Grouped-Query Attention: Each key-value head is shared by 16 query heads, which reduces KV-cache memory by 16x and makes generation faster and cheaper.
• Q-K normalization with extra query scaling: Before computing attention, Muse Glimmer applies RMS normalization to every query and key head to keep attention logits stable. After this, queries are multiplied by a scale factor to set the target logit scale after normalization. The extra query scaling behaves like an inverse temperature at the softmax level.

Perception Encoder

Muse Glimmer uses one image encoder to handle both images and videos. Unlike the relatively small vision encoders used in other VLMs, this is a sizable 2B ViT-like model designed after the Perception Encoder architecture. Perception Encoder was previously introduced by Meta as a backbone for various downstream spatial and multimodal tasks.
The encoder patchifies images to a shape of 2 frames x 3 channels x 14 x 14, and passes them through a linear layer for projection. An interpolated absolute position embedding from a learned position table is then added to these embeddings. These are then sent to the vision tower which consist of 50 layers and GELU MLPs. Similar to the language model, the attention pattern consists of three window attention layers followed by one full attention layer. Inside the attention layers, 2D RoPE is applied to the queries and keys.

After transformer, pixel shuffle concatenates 2x2 groups of neighboring spatial tokens which reduces the number of image tokens 4x without discarding their channels. The merged features are then projected to the shared embedding space of the text decoder.

Videos go through the same encoder frame by frame, where each frame is converted into patches (of shape [batch, temporal groups, grid height, grid width, 2 frames, 3 channels, 14, 14]). The processor targets 2 frames per second and caps the clip at 96 frames sampled evenly across video. The processor creates timestamped video placeholders, interleaving text with frame e.g. “Time: 0.0s <|video|> x N” in which the final video embeddings are replaced before the final projection layer.

Transformers

Upgrade transformers to the latest version to be able to use Muse Glimmer.

pip install --upgrade transformers accelerate

Muse Glimmer comes with day-0 support in transformers, both for the main model and the speculative decoding drafter. You can use AutoModelForMultimodalLM and AutoProcessor classes to load the model and the processor.

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

The same snippet runs unchanged on NVIDIA (CUDA), AMD (ROCm) and Intel (XPU) GPUs, device_map="auto" places the model on whichever accelerator is available.

Text-only Inference

After loading the model, you can do text-only inference with it as follows.

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)


messages = [
    {"role": "user", "content": "Write a short joke about saving RAM."},
]


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


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

Prompting the model with images and text

We would need torchvision to be able to use images and text.

pip install torchvision

Muse Glimmer accepts images as input, as demonstrated here:

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)


messages = [
    {
        "role": "user", "content": [
            {"type": "image", "image": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/SF.png"},
            {"type": "text", "text": "What is shown in this image?"}
        ]
    }
]

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


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

Video Inference

To work with videos we recommend installing torchcodec into the environment.

pip install torchcodec

Muse Glimmer can answer complex questions about videos without audio. You can do video inference as follows, here’s an example from VideoMME2, which is the most popular video question answering benchmark.

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)


messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/IMG_8137.mp4"},
            {"type": "text", "text": "Describe what happens in this video."},
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    reasoning_strength="low",
    processor_kwargs={"num_frames": 96},
).to(model.device)

input_len = inputs["input_ids"].shape[-1]
outputs = model.generate(**inputs)

response = processor.decode(
    outputs[0, input_len:],
    skip_special_tokens=False,
)
print(response)

Multimodal tool calling

Muse Glimmer can do multimodal tool calling, here’s how you can do it. In the example below, we ask the model to call the weather tool based on the city in the image.

import json
import re
from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "weather.get",
            "description": "Get the current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"},
                },
                "required": ["city"],
            },
        },
    }
]


messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/SF.png"},
            {"type": "text", "text": "I'm going to the city in this picture. What clothes should I wear?"},
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tools=tools,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    reasoning_strength="low"
).to(model.device)

input_len = inputs["input_ids"].shape[-1]
outputs = model.generate(**inputs)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
print(response)

Object Detection

You can use Muse Glimmer to do open ended object detection in images as follows.

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "meta-models/Muse-Glimmer-30B"


processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/SF.png"},
        {
            "type": "text",
            "text": (