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

Libraries

How to use MetaphoricalCode/Gemma-3-Glitter-27B-exl3-3bpw-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-exl3-3bpw-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-exl3-3bpw-hb6")
model = AutoModelForMultimodalLM.from_pretrained("MetaphoricalCode/Gemma-3-Glitter-27B-exl3-3bpw-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-exl3-3bpw-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-exl3-3bpw-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-exl3-3bpw-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-exl3-3bpw-hb6

SGLang

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

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

File size: 1,563 Bytes

fd251c0

---
base_model:
- allura-org/Gemma-3-Glitter-27B
base_model_relation: quantized
library_name: transformers
tags:
- mergekit
- merge
---
## Quantized using the default exllamav3 (0.0.3) quantization process.

- Original model: https://huggingface.co/allura-org/Gemma-3-Glitter-27B
- exllamav3: https://github.com/turboderp-org/exllamav3
---
# ✨G3 Glitter 27B✨
<figure>
  <img src="https://huggingface.co/ToastyPigeon/Gemma-3-Glitter-27B/resolve/main/ComfyUI_02512_.png" width="600">
</figure>

A creative writing model based on Gemma 3 27B. 

[Columbidae/gemma-3-27b-half](https://huggingface.co/Columbidae/gemma-3-27b-half), a 50/50 merge of 27B IT and 27B PT, was used as the base model. (This was done because of the success of [Starshine](https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B), a 50/50 IT and PT merge.)

The inclusion of PT model does weaken the instruct, but it also weakens the censorship/hesitancy to participate in certain fictional stories. The prose also becomes more natural with less of the IT model included. 

**This model does better with short and to-the-point prompts. Long, detailed system prompts will often confuse it.** (Tested with 1000-2000 token system prompts to lackluster results compared to 100-500 token prompts).

## Instruct Format

Uses Gemma2/3 instruct and context. Like Glitter 12b, this works well with `temp = 1, top-nsigma = 1.5`. 
```
<start_of_turn>user
{User messages; can also put sysprompt here to use the built-in g3 training}<end_of_turn>
<start_of_turn>model
{model response}<end_of_turn>
```