How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/gemma-4-E4B-it-Uncensored-MAX-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": "prithivMLmods/gemma-4-E4B-it-Uncensored-MAX-GGUF",
		"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/prithivMLmods/gemma-4-E4B-it-Uncensored-MAX-GGUF:
Quick Links

gemma-4-E4B-it-Uncensored-MAX-GGUF

gemma-4-E4B-it-Uncensored-MAX is an uncensored evolution built on top of google/gemma-4-E4B-it. This model applies advanced refusal direction analysis and abliteration-based training strategies to significantly reduce internal refusal behaviors while preserving the reasoning and instruction-following strengths of the original architecture. The result is a powerful E4B parameter language model optimized for detailed responses and improved instruction adherence.

Model Files

File Name Quant Type File Size File Link
gemma-4-E4B-it-Uncensored-MAX.BF16.gguf BF16 15.1 GB Download
gemma-4-E4B-it-Uncensored-MAX.F16.gguf F16 15.1 GB Download
gemma-4-E4B-it-Uncensored-MAX.F32.gguf F32 30.1 GB Download
gemma-4-E4B-it-Uncensored-MAX.Q8_0.gguf Q8_0 8.01 GB Download
gemma-4-E4B-it-Uncensored-MAX.mmproj-bf16.gguf mmproj-bf16 992 MB Download
gemma-4-E4B-it-Uncensored-MAX.mmproj-f16.gguf mmproj-f16 992 MB Download
gemma-4-E4B-it-Uncensored-MAX.mmproj-f32.gguf mmproj-f32 1.91 GB Download
gemma-4-E4B-it-Uncensored-MAX.mmproj-q8_0.gguf mmproj-q8_0 560 MB Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

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GGUF
Model size
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Architecture
gemma4
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