Instructions to use tiny-random/gemma-4-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/gemma-4-e with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/gemma-4-e")
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("tiny-random/gemma-4-e")
model = AutoModelForImageTextToText.from_pretrained("tiny-random/gemma-4-e")
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 tiny-random/gemma-4-e with vLLM:

Install from pip and serve model

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

SGLang

How to use tiny-random/gemma-4-e 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 "tiny-random/gemma-4-e" \
    --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": "tiny-random/gemma-4-e",
		"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 "tiny-random/gemma-4-e" \
        --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": "tiny-random/gemma-4-e",
		"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 tiny-random/gemma-4-e with Docker Model Runner:
```
docker model run hf.co/tiny-random/gemma-4-e
```

gemma-4-e / README.md

yujiepan

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	---
	library_name: transformers
	base_model:
	- google/gemma-4-E4B-it
	---

	This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it).

	\| File path \| Size \|
	\|------\|------\|
	\| model.safetensors \| 9.5MB \|


	### Example usage:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoProcessor

	model_id = "tiny-random/gemma-4-e"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, dtype=torch.bfloat16, device_map="auto"
	)
	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "audio",
	"audio": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav",
	},
	{"type": "text", "text": "Transcribe the following speech segment."},
	],
	},
	{
	"role": "assistant",
	"content": [{"type": "text", "text": "Dummy response for audio"}],
	},
	{
	"role": "user",
	"content": [
	{
	"type": "image",
	"url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/GoldenGate.png",
	},
	{"type": "text", "text": "What is shown in this image?"},
	],
	},
	{
	"role": "assistant",
	"content": [{"type": "text", "text": "Dummy response for image"}],
	},
	{
	"role": "user",
	"content": [
	{
	"type": "video",
	"video": "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/ForBiggerBlazes.mp4",
	},
	{"type": "text", "text": "Describe this video."},
	],
	},
	]
	inputs = processor.apply_chat_template(
	messages,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
	add_generation_prompt=True,
	).to(model.device)
	input_len = inputs["input_ids"].shape[-1]
	print("input_len:", input_len)
	outputs = model.generate(**inputs, max_new_tokens=32)
	response = processor.decode(outputs[0], skip_special_tokens=False)
	response = response.replace("<\|audio\|>", "A")
	response = response.replace("<\|image\|>", "I")
	response = response.replace("<\|video\|>", "V")
	print(response)
	```

	### Codes to create this repo:

	<details>
	<summary>Click to expand</summary>

	```python
	import json
	from pathlib import Path

	import torch
	from huggingface_hub import file_exists, hf_hub_download

	from transformers import (
	AutoConfig,
	AutoModelForCausalLM,
	AutoProcessor,
	AutoTokenizer,
	Gemma4ForConditionalGeneration,
	GenerationConfig,
	set_seed,
	)

	source_model_id = "google/gemma-4-E4B-it"
	save_folder = "/tmp/tiny-random/gemma-4-e"

	processor = AutoProcessor.from_pretrained(source_model_id)
	processor.save_pretrained(save_folder)

	with open(
	hf_hub_download(source_model_id, filename="config.json", repo_type="model"),
	"r",
	encoding="utf-8",
	) as f:
	config_json = json.load(f)

	config_json["audio_config"].update(
	{
	"num_attention_heads": 2,
	"num_hidden_layers": 2,
	"hidden_size": 64,
	"output_proj_dims": 32,
	}
	)
	config_json["text_config"].update(
	{
	"global_head_dim": 64,
	"head_dim": 32,
	"hidden_size": 8,
	"hidden_size_per_layer_input": 2,
	"intermediate_size": 64,
	"layer_types": [
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	],
	"num_attention_heads": 8,
	"num_hidden_layers": 4,
	"num_key_value_heads": 4,
	"num_kv_shared_layers": 2,
	}
	)
	config_json["vision_config"].update(
	{
	"num_hidden_layers": 2,
	"hidden_size": 8,
	"intermediate_size": 64,
	"head_dim": 32,
	"global_head_dim": 32,
	"num_attention_heads": 4,
	"num_key_value_heads": 4,
	}
	)

	with open(f"{save_folder}/config.json", "w", encoding="utf-8") as f:
	json.dump(config_json, f, indent=2)

	config = AutoConfig.from_pretrained(
	save_folder,
	trust_remote_code=True,
	)
	print(config)

	torch.set_default_dtype(torch.bfloat16)
	model = Gemma4ForConditionalGeneration(config)
	torch.set_default_dtype(torch.float32)
	if file_exists(
	filename="generation_config.json", repo_id=source_model_id, repo_type="model"
	):
	model.generation_config = GenerationConfig.from_pretrained(
	source_model_id,
	trust_remote_code=True,
	)
	set_seed(42)
	model = model.cpu()
	all_numels = 0
	for name, p in sorted(model.named_parameters()):
	all_numels += p.numel()
	with torch.no_grad():
	for name, p in sorted(model.named_parameters()):
	torch.nn.init.normal_(p, 0, 0.2)
	print(name, p.shape, f"{p.numel() / all_numels * 100: .4f}%")
	model.save_pretrained(save_folder)
	```

	</details>

	### Printing the model:

	<details><summary>Click to expand</summary>

	```text
	Gemma4ForConditionalGeneration(
	(model): Gemma4Model(
	(language_model): Gemma4TextModel(
	(embed_tokens): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0)
	(layers): ModuleList(
	(0): Gemma4TextDecoderLayer(
	(self_attn): Gemma4TextAttention(
	(q_norm): Gemma4RMSNorm()
	(k_norm): Gemma4RMSNorm()
	(v_norm): Gemma4RMSNorm()
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(q_proj): Linear(in_features=8, out_features=256, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	)
	(mlp): Gemma4TextMLP(
	(gate_proj): Linear(in_features=8, out_features=64, bias=False)
	(up_proj): Linear(in_features=8, out_features=64, bias=False)
	(down_proj): Linear(in_features=64, out_features=8, bias=False)
	(act_fn): GELUTanh()
	)
	(input_layernorm): Gemma4RMSNorm()
	(post_attention_layernorm): Gemma4RMSNorm()
	(pre_feedforward_layernorm): Gemma4RMSNorm()
	(post_feedforward_layernorm): Gemma4RMSNorm()
	(act_fn): GELUTanh()
	(per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False)
	(per_layer_projection): Linear(in_features=2, out_features=8, bias=False)
	(post_per_layer_input_norm): Gemma4RMSNorm()
	)
	(1): Gemma4TextDecoderLayer(
	(self_attn): Gemma4TextAttention(
	(q_norm): Gemma4RMSNorm()
	(k_norm): Gemma4RMSNorm()
	(v_norm): Gemma4RMSNorm()
	(k_proj): Linear(in_features=8, out_features=256, bias=False)
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(v_proj): Linear(in_features=8, out_features=256, bias=False)
	(o_proj): Linear(in_features=512, out_features=8, bias=False)
	)
	(mlp): Gemma4TextMLP(
	(gate_proj): Linear(in_features=8, out_features=64, bias=False)
	(up_proj): Linear(in_features=8, out_features=64, bias=False)
	(down_proj): Linear(in_features=64, out_features=8, bias=False)
	(act_fn): GELUTanh()
	)
	(input_layernorm): Gemma4RMSNorm()
	(post_attention_layernorm): Gemma4RMSNorm()
	(pre_feedforward_layernorm): Gemma4RMSNorm()
	(post_feedforward_layernorm): Gemma4RMSNorm()
	(act_fn): GELUTanh()
	(per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False)
	(per_layer_projection): Linear(in_features=2, out_features=8, bias=False)
	(post_per_layer_input_norm): Gemma4RMSNorm()
	)
	(2): Gemma4TextDecoderLayer(
	(self_attn): Gemma4TextAttention(
	(q_norm): Gemma4RMSNorm()
	(k_norm): Gemma4RMSNorm()
	(v_norm): Gemma4RMSNorm()
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(q_proj): Linear(in_features=8, out_features=256, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	)
	(mlp): Gemma4TextMLP(
	(gate_proj): Linear(in_features=8, out_features=64, bias=False)
	(up_proj): Linear(in_features=8, out_features=64, bias=False)
	(down_proj): Linear(in_features=64, out_features=8, bias=False)
	(act_fn): GELUTanh()
	)
	(input_layernorm): Gemma4RMSNorm()
	(post_attention_layernorm): Gemma4RMSNorm()
	(pre_feedforward_layernorm): Gemma4RMSNorm()
	(post_feedforward_layernorm): Gemma4RMSNorm()
	(act_fn): GELUTanh()
	(per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False)
	(per_layer_projection): Linear(in_features=2, out_features=8, bias=False)
	(post_per_layer_input_norm): Gemma4RMSNorm()
	)
	(3): Gemma4TextDecoderLayer(
	(self_attn): Gemma4TextAttention(
	(q_norm): Gemma4RMSNorm()
	(k_norm): Gemma4RMSNorm()
	(v_norm): Gemma4RMSNorm()
	(k_proj): Linear(in_features=8, out_features=256, bias=False)
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(v_proj): Linear(in_features=8, out_features=256, bias=False)
	(o_proj): Linear(in_features=512, out_features=8, bias=False)
	)
	(mlp): Gemma4TextMLP(
	(gate_proj): Linear(in_features=8, out_features=64, bias=False)
	(up_proj): Linear(in_features=8, out_features=64, bias=False)
	(down_proj): Linear(in_features=64, out_features=8, bias=False)
	(act_fn): GELUTanh()
	)
	(input_layernorm): Gemma4RMSNorm()
	(post_attention_layernorm): Gemma4RMSNorm()
	(pre_feedforward_layernorm): Gemma4RMSNorm()
	(post_feedforward_layernorm): Gemma4RMSNorm()
	(act_fn): GELUTanh()
	(per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False)
	(per_layer_projection): Linear(in_features=2, out_features=8, bias=False)
	(post_per_layer_input_norm): Gemma4RMSNorm()
	)
	)
	(norm): Gemma4RMSNorm()
	(rotary_emb): Gemma4TextRotaryEmbedding()
	(embed_tokens_per_layer): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0)
	(per_layer_model_projection): Linear(in_features=8, out_features=8, bias=False)
	(per_layer_projection_norm): Gemma4RMSNorm()
	)
	(vision_tower): Gemma4VisionModel(
	(patch_embedder): Gemma4VisionPatchEmbedder(
	(input_proj): Linear(in_features=768, out_features=8, bias=False)
	)
	(encoder): Gemma4VisionEncoder(
	(rotary_emb): Gemma4VisionRotaryEmbedding()
	(layers): ModuleList(
	(0-1): 2 x Gemma4VisionEncoderLayer(
	(self_attn): Gemma4VisionAttention(
	(q_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=8, out_features=128, bias=False)
	)
	(k_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=8, out_features=128, bias=False)
	)
	(v_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=8, out_features=128, bias=False)
	)
	(o_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=128, out_features=8, bias=False)
	)
	(q_norm): Gemma4RMSNorm()
	(k_norm): Gemma4RMSNorm()
	(v_norm): Gemma4RMSNorm()
	)
	(mlp): Gemma4VisionMLP(
	(gate_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=8, out_features=64, bias=False)
	)
	(up_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=8, out_features=64, bias=False)
	)
	(down_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=8, bias=False)
	)
	(act_fn): GELUTanh()
	)
	(input_layernorm): Gemma4RMSNorm()
	(post_attention_layernorm): Gemma4RMSNorm()
	(pre_feedforward_layernorm): Gemma4RMSNorm()
	(post_feedforward_layernorm): Gemma4RMSNorm()
	)
	)
	)
	(pooler): Gemma4VisionPooler()
	)
	(embed_vision): Gemma4MultimodalEmbedder(
	(embedding_projection): Linear(in_features=8, out_features=8, bias=False)
	(embedding_pre_projection_norm): Gemma4RMSNorm()
	)
	(audio_tower): Gemma4AudioModel(
	(subsample_conv_projection): Gemma4AudioSubSampleConvProjection(
	(layer0): Gemma4AudioSubSampleConvProjectionLayer(
	(conv): Conv2d(1, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
	(norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
	(act): ReLU()
	)
	(layer1): Gemma4AudioSubSampleConvProjectionLayer(
	(conv): Conv2d(128, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
	(norm): LayerNorm((32,), eps=1e-06, elementwise_affine=True)
	(act): ReLU()
	)
	(input_proj_linear): Linear(in_features=1024, out_features=64, bias=False)
	)
	(rel_pos_enc): Gemma4AudioRelPositionalEncoding()
	(layers): ModuleList(
	(0-1): 2 x Gemma4AudioLayer(
	(feed_forward1): Gemma4AudioFeedForward(
	(ffw_layer_1): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=256, bias=False)
	)
	(ffw_layer_2): Gemma4ClippableLinear(
	(linear): Linear(in_features=256, out_features=64, bias=False)
	)
	(pre_layer_norm): Gemma4RMSNorm()
	(post_layer_norm): Gemma4RMSNorm()
	(act_fn): SiLUActivation()
	)
	(feed_forward2): Gemma4AudioFeedForward(
	(ffw_layer_1): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=256, bias=False)
	)
	(ffw_layer_2): Gemma4ClippableLinear(
	(linear): Linear(in_features=256, out_features=64, bias=False)
	)
	(pre_layer_norm): Gemma4RMSNorm()
	(post_layer_norm): Gemma4RMSNorm()
	(act_fn): SiLUActivation()
	)
	(self_attn): Gemma4AudioAttention(
	(q_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=64, bias=False)
	)
	(k_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=64, bias=False)
	)
	(v_proj): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=64, bias=False)
	)
	(post): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=64, bias=False)
	)
	(relative_k_proj): Linear(in_features=64, out_features=64, bias=False)
	)
	(lconv1d): Gemma4AudioLightConv1d(
	(linear_start): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=128, bias=False)
	)
	(linear_end): Gemma4ClippableLinear(
	(linear): Linear(in_features=64, out_features=64, bias=False)
	)
	(depthwise_conv1d): Gemma4AudioCausalConv1d(64, 64, kernel_size=(5,), stride=(1,), groups=64, bias=False)
	(pre_layer_norm): Gemma4RMSNorm()
	(conv_norm): Gemma4RMSNorm()
	(act_fn): SiLUActivation()
	)
	(norm_pre_attn): Gemma4RMSNorm()
	(norm_post_attn): Gemma4RMSNorm()
	(norm_out): Gemma4RMSNorm()
	)
	)
	(output_proj): Linear(in_features=64, out_features=32, bias=True)
	)
	(embed_audio): Gemma4MultimodalEmbedder(
	(embedding_projection): Linear(in_features=32, out_features=8, bias=False)
	(embedding_pre_projection_norm): Gemma4RMSNorm()
	)
	)
	(lm_head): Linear(in_features=8, out_features=262144, bias=False)
	)
	```

	</details>

	### Test environment:

	- torch: 2.11.0
	- transformers: 5.5.0