Instructions to use hexoy/gemma-4-e2b-monarch-35mlp-lora-r8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hexoy/gemma-4-e2b-monarch-35mlp-lora-r8 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-monarch-35mlp-lora-r8", 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-monarch-35mlp-lora-r8", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hexoy/gemma-4-e2b-monarch-35mlp-lora-r8 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-monarch-35mlp-lora-r8"
# 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-monarch-35mlp-lora-r8",
		"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-monarch-35mlp-lora-r8

SGLang

How to use hexoy/gemma-4-e2b-monarch-35mlp-lora-r8 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-monarch-35mlp-lora-r8" \
    --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-monarch-35mlp-lora-r8",
		"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-monarch-35mlp-lora-r8" \
        --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-monarch-35mlp-lora-r8",
		"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-monarch-35mlp-lora-r8 with Docker Model Runner:
```
docker model run hf.co/hexoy/gemma-4-e2b-monarch-35mlp-lora-r8
```

Gemma 4 E2B Distilled + LoRA R8

Experimental benchmark-recovery model. It adds native rank-8, alpha-16 LoRA residuals to all 105 Monarch MLP projections and loads without PEFT. The model has 3,691,669,024 parameters, including 9,400,320 LoRA parameters.

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-monarch-35mlp-lora-r8"
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 standalone export passed exact LoRA state and BF16 logit round trips plus a clean-cache load. Corrected 64-token generation repeated phrases for text and produced malformed or inaccurate answers on all three image tests. Treat this as a benchmark result, not a general text or multimodal recovery release.

Reproducibility

Base revision: f897353fca328b1cc5fd2e12d645773ca637f5f0
LoRA source commit: 1435571b20dd26c073a535678975884154add5b8
BF16 source: hexoy/gemma-4-e2b-distilled
INT8 variant: hexoy/gemma-4-e2b-monarch-35mlp-int8

Detailed training and release evidence is retained privately.

This repository contains custom modeling code. Review it before enabling trust_remote_code=True. This is an experimental derivative, not an official Google release.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for hexoy/gemma-4-e2b-monarch-35mlp-lora-r8

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

hexoy/gemma-4-e2b-distilled

Adapter

(1)

this model