Instructions to use hexoy/gemma-4-e2b-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hexoy/gemma-4-e2b-distilled with Transformers:

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

pipe = pipeline("image-text-to-text", model="hexoy/gemma-4-e2b-distilled", trust_remote_code=True)
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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("hexoy/gemma-4-e2b-distilled", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hexoy/gemma-4-e2b-distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hexoy/gemma-4-e2b-distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hexoy/gemma-4-e2b-distilled",
		"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/hexoy/gemma-4-e2b-distilled

SGLang

How to use hexoy/gemma-4-e2b-distilled 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 "hexoy/gemma-4-e2b-distilled" \
    --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": "hexoy/gemma-4-e2b-distilled",
		"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 "hexoy/gemma-4-e2b-distilled" \
        --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": "hexoy/gemma-4-e2b-distilled",
		"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 hexoy/gemma-4-e2b-distilled with Docker Model Runner:
```
docker model run hf.co/hexoy/gemma-4-e2b-distilled
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma 4 E2B Distilled

Distilled Gemma 4 E2B with all 35 language-model MLPs replaced by two-factor Monarch maps. The released BF16 model has 3,682,268,704 parameters, instead of 5,104,297,504 in the original Gemma 4 E2B.

1. Weight Storage

Model	Status	Loaded weights	Serialized weights	Reduction vs dense BF16
Dense Gemma 4 BF16	Reference	9.507 GiB	9.543 GiB	-
Distilled Gemma 4 BF16	Released	6.859 GiB	6.859 GiB	27.86%
Distilled Gemma 4 + LoRA r8 BF16	Experimental	6.876 GiB	6.877 GiB	27.68%
Distilled Gemma 4 + INT8 linears	Released	6.135 GiB	6.136 GiB	35.48%

BF16 sizes use two bytes per parameter; serialized LoRA size was audited from the two safetensor shards. Values exclude activations and CUDA workspaces.

2. TinyHellaSwag

All rows used the same RTX PRO 6000, fixed batch size 32, seed 1234, 100 official examples, 10-shot prompts, and no chat template.

Model	Status	GP-IRT accuracy	Raw accuracy	Runtime	Peak VRAM
Dense Gemma 4 BF16	Reference	39.23%	29%	28.50 s	57.88 GiB
Distilled Gemma 4 BF16	Released	32.35%	22%	23.33 s	55.23 GiB
Distilled Gemma 4 + LoRA r8 BF16	Experimental	33.25%	23%	24.93 s	55.25 GiB
Distilled Gemma 4 + INT8 linears	Released	30.57%	21%	23.14 s	54.51 GiB

Rank-8 LoRA improved the 35-layer source by 0.90 GP-IRT points and one correct item, but missed the former +1.0 point or +2 item release gate. The 100-example benchmark is noisy.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "hexoy/gemma-4-e2b-distilled"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
)

The rank-8 result is published separately at hexoy/gemma-4-e2b-monarch-35mlp-lora-r8. It is a benchmark-recovery experiment: corrected 64-token generation repeated phrases for text and remained malformed or inaccurate for images. It is not a general text or multimodal recovery release.