How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArtusDev/allura-org_Gemma-3-Glitter-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/allura-org_Gemma-3-Glitter-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/ArtusDev/allura-org_Gemma-3-Glitter-4B-GGUF:
Quick Links

ArtusDev/allura-org_Gemma-3-Glitter-4B-GGUF

โœจ Overview :3

This repository contains GGUF format model files converted from allura-org/Gemma-3-Glitter-4B.

The conversion was performed by ArtusDev using llama.cpp, specifically utilizing the imatrix quantization option for potentially improved performance.

๐Ÿ“„ Original Model Details ^_^

For more information about the model please refer to the original model card. It's pretty neat (empty)!

๐Ÿ’ฌ Instruct Format >.<

This model uses a custom Gemma 2/3 instruct format. It has been trained to recognize an optional system role.

<start_of_turn>system
{optional system prompt here}<end_of_turn>
<start_of_turn>user
{User messages. You can also place the system prompt here.}<end_of_turn>
<start_of_turn>model
{Model's response}<end_of_turn>

Note: Always ensure the format strictly adheres to the required tokens and structure for optimal model performance. Don't mess it up :3!

GGUF Quantizations (imatrix) by ArtusDev >:3
Downloads last month
95
GGUF
Model size
4B params
Architecture
gemma3
Hardware compatibility
Log In to add your hardware

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support

Model tree for ArtusDev/allura-org_Gemma-3-Glitter-4B-GGUF

Quantized
(4)
this model