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
```

gemma-4-e2b-distilled / README.md

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	---
	license: apache-2.0
	base_model: google/gemma-4-E2B-it
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- gemma4
	- monarch-matrices
	- model-compression
	---

	# 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

	```python
	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`](https://huggingface.co/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.

	## Reproducibility

	- BF16 model revision: `f897353fca328b1cc5fd2e12d645773ca637f5f0`
	- GitHub repository: [`ratmir-miftachov/gemma-distillation`](https://github.com/ratmir-miftachov/gemma-distillation)
	- INT8 variant: [`hexoy/gemma-4-e2b-monarch-35mlp-int8`](https://huggingface.co/hexoy/gemma-4-e2b-monarch-35mlp-int8)
	- Experimental LoRA r8 variant: [`hexoy/gemma-4-e2b-monarch-35mlp-lora-r8`](https://huggingface.co/hexoy/gemma-4-e2b-monarch-35mlp-lora-r8)
	- LoRA source commit: `1435571b20dd26c073a535678975884154add5b8`

	Detailed training and release evidence is retained privately.

	Derived from [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it).
	See `NOTICE` for the modification summary.