Instructions to use electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128")
model = AutoModelForMultimodalLM.from_pretrained("electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128

SGLang

How to use electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 with Docker Model Runner:
```
docker model run hf.co/electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128
```

Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128

AutoRound W4A16 (4-bit weights / 16-bit activations, group_size=128, symmetric) quantization of electroglyph/Muse-Glimmer-30B-uncensored2.

Tool: Intel AutoRound (scheme="W4A16", format="auto_round")
Real SignRound tuning on calibration data (not RTN).
Calibration: HuggingFaceH4/ultrachat_200k:num=384,nvidia/OpenCodeInstruct:num=128 (nsamples=512, seqlen=2048, iters=1000).

Quantization config

{
  "scheme": "W4A16",
  "format": "auto_round",
  "bits": 4,
  "group_size": 128,
  "sym": true,
  "nsamples": 512,
  "seqlen": 2048,
  "iters": 1000,
  "lr": auto,
  "batch_size": 8,
  "gradient_accumulate_steps": 1,
  "dataset": "HuggingFaceH4/ultrachat_200k:num=384,nvidia/OpenCodeInstruct:num=128",
  "device_map": "auto",
  "model_dtype": "bf16",
  "low_gpu_mem_usage": false,
  "low_cpu_mem_usage": true,
  "enable_torch_compile": true,
  "seed": 42
}

Run with vLLM (native kernel, no `--quantization` flag)

auto_round W4A16 is in vLLM's Intel Quantization Support (W4A16 weight-only) and is served directly:

pip install vllm
vllm serve electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128 --gpu-memory-utilization 0.8 --max-model-len 32768

Python:

from vllm import LLM, SamplingParams
llm = LLM(model="electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128")
print(llm.generate(["Hello, my name is"], SamplingParams(temperature=0.6, top_p=0.95))[0].outputs[0].text)

Run with Transformers

pip install -U "transformers>=5.22.2" auto-round  # auto-round registers the auto_round format

from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128")
model = AutoModelForImageTextToText.from_pretrained("electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128", device_map="auto", torch_dtype="auto")

Best practice sampling (per the source model card): temperature=1.0, top_p=0.95, top_k=64. Reasoning strength is set in the system prompt (Reasoning strength: <low|medium|high|xhigh>); use high/xhigh for coding and agentic tasks.

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

I32

BF16

F16

F32

Model tree for electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128

Base model

meta-models/Muse-Glimmer-30B

Finetuned

electroglyph/Muse-Glimmer-30B-uncensored2

Quantized

(1)

this model

Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128

Quantization config

Run with vLLM (native kernel, no --quantization flag)

Run with Transformers

Model tree for electroglyph/Muse-Glimmer-30B-uncensored2-AutoRound-W4A16-G128

Run with vLLM (native kernel, no `--quantization` flag)