Meta、ローカル・エージェント型マルチモーダル AI「Muse Glimmer」をオープンソースで公開

pip install --upgrade transformers accelerate

from transformers import AutoProcessor, AutoModelForMultimodalLM

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

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

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

# Process input
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]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
print(response)

pip install torchvision

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]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
print(response)

import json
import re

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, max_new_tokens=128)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

parsed = processor.tokenizer.parse_response(response)

import json

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/SF.png"},
        {
            "type": "text",
            "text": (
                "Detect the bridge. Return only the detection in the model's "
                "native object-detection format, with no explanation."
            ),
        },
    ],
}]

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

input_len = inputs["input_ids"].shape[-1]
outputs = model.generate(**inputs, max_new_tokens=128)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

detections = json.loads(response.removesuffix("<|eot|>"))
print(detections)
# [{"x_min": 0, "y_min": 390, "x_max": 520, "y_max": 603}]

# note that you need to scale X and Y values to image size to visualize:
xyxy = (
round(box["x_min"] / 1000 * width),
round(box["y_min"] / 1000 * height),
round(box["x_max"] / 1000 * width),
round(box["y_max"] / 1000 * height),
)

pip install torchcodec

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {
        "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, max_new_tokens=1024)

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

parsed = processor.parse_response(
    response,
    prefix=inputs["input_ids"],
)
print(parsed)

curl -LsSf https://llama.app/install.sh | sh

llama serve meta-models/Muse-Glimmer-30B-GGUF

curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Write a limerick about python exceptions"}
        ]
    }'

import torch
from transformers import AutoProcessor, MuseGlimmerAssistantModel, MuseGlimmerForConditionalGeneration

model_id = "meta-models/Muse-Glimmer-30B"
target = MuseGlimmerForConditionalGeneration.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
assistant = MuseGlimmerAssistantModel.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

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

out = target.generate(**inputs, assistant_model=assistant, speculation_type="dflash", max_new_tokens=64, do_sample=True)
print(processor.batch_decode(out)[0])

llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --spec-type draft-dflash --spec-draft-n-max 15

llama cli -hf meta-models/Muse-Glimmer-30B-GGUF --spec-type draft-dflash

# tensor parallel serving across 4 GPUs
vllm serve meta-models/Muse-Glimmer-30B --model-impl transformers --tensor-parallel-size 4

# infer
curl -s http://127.0.0.1:8000/v1/chat/completions \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "username/muse-glimmer-hf-v2",
      "messages": [
        {"role": "user", "content": "Explain tensor parallelism briefly."}
      ],
      "temperature": 0.0,
      "max_tokens": 256
    }'

{
  models: {
    mode: "merge",
    providers: {
      muse: {
        baseUrl: "https://YOUR-ENDPOINT.endpoints.huggingface.cloud/v1",
        apiKey: {
          source: "env",
          provider: "default",
          id: "HF_TOKEN"
        },
        api: "openai-completions",
        authHeader: true,
        models: [{
          id: "meta/Muse-Glimmer-30B",
          name: "Muse Glimmer",
          reasoning: false,
          input: ["text", "image"],
          contextWindow: 32768,
          maxTokens: 8192
        }]
      }
    }
  },
  agents: {
    defaults: {
      model: { primary: "muse/meta/Muse-Glimmer-30B" }
    }
  }
}

openclaw gateway restart

openclaw agent --message "Reply with: muse-ready"

## Local model deployment

When asked to deploy locally, perform the work; do not give instructions.

1. Inspect hardware and the Hugging Face cache.
2. Search the Hub for compatible GGUF weights using `apps=llama.cpp`; confirm exact filenames through the model-tree API.
3. Prefer an existing suitable GGUF, normally `Q4_K_M`. Treat `mmproj-*.gguf` as projector weights.
4. If no GGUF exists, download the source weights, convert with `convert_hf_to_gguf.py`, then quantize with `llama-quantize`.
5. Preserve source weights and record the repository, revision, filenames, and quantization.
6. Start `llama-server` with an `onyx` alias and an OpenAI-compatible endpoint.
7. Validate `/v1/models` and `/v1/chat/completions`, requiring non-empty, correct content.
8. Report concise progress and logs. Claim completion only after validation passes.

## Hugging Face Inference Endpoint deployment

When asked to deploy on Hugging Face Inference Endpoints, perform the work; do
not give instructions.

1. Inspect Hugging Face authentication, the current model repository, and any
   existing endpoints.
2. Confirm the exact model repository and immutable revision through the Hub
   API; inspect its architecture, configuration, and chat template.
3. Confirm that the model is supported by vLLM, then deploy or update a
   protected Inference Endpoint using the managed native vLLM engine.
4. Choose an available region and the smallest suitable accelerator. Use one
   replica and enable scale-to-zero when supported.
5. Preserve the previous endpoint configuration for rollback. Do not expose
   tokens, publish private weights, or replace an unrelated endpoint.
6. Wait for the endpoint to become ready. If startup fails, inspect the logs
   and report the actual blocker rather than repeatedly changing settings.
7. Validate `/health`, `/v1/models`, and `/v1/chat/completions`, requiring the
   expected model and non-empty, correct content. When agent use is required,
   also validate a real structured tool call.
8. Configure the Claw agent to use the endpoint's OpenAI-compatible `/v1` URL,
   storing credentials as secrets and retaining the previous provider as
   rollback. Test the connection in a fresh session.
9. Report concise progress and finish with the repository, revision, engine,
   hardware, endpoint URL, scaling state, and validation results. Claim
   completion only after every required check passes.

You are Muse Glimmer acting as an autonomous inference-optimization engineer for your own serving stack.

Goal: maximize valid single-H100 aggregate completion throughput in tokens/second.

Protocol:
1. Establish a correctness-passing baseline.
2. Test one reversible optimization at a time.
3. Keep the prompt, concurrency, sampling, request count, warm-up, and decode length fixed.
4. Reject results that fail correctness or prefix checks.
5. Record every experiment chronologically with its configuration, raw throughput, correctness, and delta.
6. Keep improvements and revert regressions.
7. Stop after six consecutive regressions or when the experiment budget is exhausted.
8. Report the best valid configuration and exact reproduction command.

Create a minimal scientific animation of the results:
- white background;
- raw tokens/second—never normalize;
- one point revealed per experiment;
- connect every point chronologically;
- begin with the lowest valid result;
- stop at the best result;
- export as a GIF.

Never fabricate, interpolate, or count correctness-failing measurements.