Instructions to use ml-intern-explorers/gemma-4-e2b-arabic-english-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ml-intern-explorers/gemma-4-e2b-arabic-english-vision with Transformers:

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

pipe = pipeline("image-text-to-text", model="ml-intern-explorers/gemma-4-e2b-arabic-english-vision")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ml-intern-explorers/gemma-4-e2b-arabic-english-vision")
model = AutoModelForImageTextToText.from_pretrained("ml-intern-explorers/gemma-4-e2b-arabic-english-vision")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ml-intern-explorers/gemma-4-e2b-arabic-english-vision with vLLM:

Install from pip and serve model

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

SGLang

How to use ml-intern-explorers/gemma-4-e2b-arabic-english-vision 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 "ml-intern-explorers/gemma-4-e2b-arabic-english-vision" \
    --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": "ml-intern-explorers/gemma-4-e2b-arabic-english-vision",
		"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 "ml-intern-explorers/gemma-4-e2b-arabic-english-vision" \
        --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": "ml-intern-explorers/gemma-4-e2b-arabic-english-vision",
		"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 ml-intern-explorers/gemma-4-e2b-arabic-english-vision with Docker Model Runner:
```
docker model run hf.co/ml-intern-explorers/gemma-4-e2b-arabic-english-vision
```

gemma-4-e2b-arabic-english-vision / README.md

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	---
	license: apache-2.0
	language:
	- ar
	- en
	base_model:
	- google/gemma-4-e2b-it
	tags:
	- gemma4
	- image-text-to-text
	- arabic
	- english
	- pruned
	- vision
	- multilingual
	library_name: transformers
	pipeline_tag: image-text-to-text
	---

	# Gemma 4 E2B — Arabic + English Vision (Pruned)

	A pruned version of [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it) optimized for Arabic and English vision-language tasks. Unnecessary language tokens and the audio encoder have been removed, resulting in a smaller and more efficient model while retaining full vision and text generation capabilities.

	## What was done

	\| Component \| Original \| Pruned \|
	\|-----------\|----------\|--------\|
	\| Vocabulary \| 262,144 tokens \| 209,836 tokens (Arabic + English + special) \|
	\| Vision encoder \| ✅ 16 layers \| ✅ KEPT \|
	\| Audio encoder \| ✅ 12 layers \| ❌ Removed \|
	\| Decoder layers \| 35 layers \| 35 layers (untouched) \|
	\| PLE tables \| 262,144 × 256 × 35 \| 209,836 × 256 × 35 \|
	\| Total size (bf16) \| 10.2 GB \| 8.5 GB \|
	\| Size reduction \| — \| 17% \|

	## Token breakdown

	- Arabic tokens kept: 8,460
	- English/Latin tokens kept: 201,096
	- Special tokens: 24
	- Byte fallbacks: 256
	- Total kept: 209,836 (80.0% of original)
	- Dropped: 52,308 (CJK, Cyrillic, Devanagari, Thai, etc.)

	## Why this exists

	Gemma 4's Per-Layer Embedding (PLE) architecture stores a separate embedding table for each of its 35 decoder layers. With 262K vocab, that's 4.7 GB of PLE tables alone (53% of the model). By pruning to Arabic+English only, we cut this to ~3.8 GB.

	The vision encoder is kept intact for image understanding tasks.

	## Benchmark: Arabic text recognition (zero-shot, no fine-tuning)

	Evaluated on [loay/ar_stage1_probe](https://huggingface.co/datasets/loay/ar_stage1_probe) — 100 samples, seed=42:

	\| Model \| Params \| VRAM \| CER ↓ \| Exact Match \|
	\|-------\|--------\|------\|-------\|-------------\|
	\| This model (pruned) \| 4.25B \| 8.5 GB \| 0.1168 \| 23/100 \|
	\| google/gemma-4-e2b-it (original) \| 5.10B \| 10.2 GB \| 0.1168 \| 23/100 \|

	Identical output on all 100 samples — pruning is lossless for Arabic+English tasks.

	## Intended use

	- Arabic + English vision-language tasks
	- Document understanding
	- Fine-tuning base for bilingual Arabic/English applications
	- Any task where CJK/Cyrillic/Indic language support is not needed

	## Usage

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model = AutoModelForImageTextToText.from_pretrained(
	"ml-agent-explorers/gemma-4-e2b-arabic-english-vision",
	dtype="bfloat16",
	device_map="auto",
	)
	processor = AutoProcessor.from_pretrained(
	"ml-agent-explorers/gemma-4-e2b-arabic-english-vision"
	)
	```

	## Base model

	- [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it)