Instructions to use MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6 with Transformers:

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

pipe = pipeline("image-text-to-text", model="MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6")
model = AutoModelForMultimodalLM.from_pretrained("MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6",
		"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/MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6

SGLang

How to use MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6 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 "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6" \
    --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": "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6",
		"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 "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6" \
        --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": "MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6",
		"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 MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6 with Docker Model Runner:
```
docker model run hf.co/MetaphoricalCode/Gemma-3-Glitter-27B-exl3-5bpw-hb6
```

Gemma-3-Glitter-27B-exl3-5bpw-hb6

File size: 1,411 Bytes

cf683ea

{
    "architectures": [
        "Gemma3ForConditionalGeneration"
    ],
    "boi_token_index": 255999,
    "eoi_token_index": 256000,
    "eos_token_id": [
        1,
        106
    ],
    "image_token_index": 262144,
    "initializer_range": 0.02,
    "mm_tokens_per_image": 256,
    "model_type": "gemma3",
    "text_config": {
        "head_dim": 128,
        "hidden_size": 5376,
        "intermediate_size": 21504,
        "model_type": "gemma3_text",
        "num_attention_heads": 32,
        "num_hidden_layers": 62,
        "num_key_value_heads": 16,
        "query_pre_attn_scalar": 168,
        "rope_scaling": {
            "factor": 8.0,
            "rope_type": "linear"
        },
        "sliding_window": 1024
    },
    "torch_dtype": "bfloat16",
    "transformers_version": "4.50.0.dev0",
    "vision_config": {
        "hidden_size": 1152,
        "image_size": 896,
        "intermediate_size": 4304,
        "model_type": "siglip_vision_model",
        "num_attention_heads": 16,
        "num_hidden_layers": 27,
        "patch_size": 14,
        "vision_use_head": false
    },
    "quantization_config": {
        "quant_method": "exl3",
        "version": "0.0.3",
        "bits": 5.0,
        "head_bits": 6,
        "calibration": {
            "rows": 100,
            "cols": 2048
        },
        "out_scales": "auto"
    }
}