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

Libraries

How to use tiny-random/gemma-4-dense 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-dense")
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-dense")
model = AutoModelForImageTextToText.from_pretrained("tiny-random/gemma-4-dense")
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-dense 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-dense"
# 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-dense",
		"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-dense

SGLang

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

yujiepan commited on 2 days ago

Commit

426311a

verified ·

1 Parent(s): 984a8fe

Upload folder using huggingface_hub

Browse files

Files changed (5) hide show

.meta.json +2 -2
README.md +3 -3
chat_template.jinja +9 -0
config.json +1 -1
generation_config.json +1 -1

.meta.json CHANGED Viewed

@@ -1,4 +1,4 @@
 {
-  "torch": "2.11.0+cu129",
-  "transformers": "5.7.0"
 }

 {
+  "torch": "2.10.0+cu130",
+  "transformers": "5.9.0"
 }

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ messages = [
         "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?"},
         ],
@@ -318,5 +318,5 @@ Gemma4ForConditionalGeneration(
 ### Test environment:
-- torch: 2.11.0+cu129
-- transformers: 5.7.0

         "content": [
             {
                 "type": "image",
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG",
             },
             {"type": "text", "text": "What is shown in this image?"},
         ],
 ### Test environment:
+- torch: 2.10.0+cu130
+- transformers: 5.9.0

chat_template.jinja CHANGED Viewed

@@ -295,6 +295,15 @@
                                 {%- endif -%}
                             {%- endfor -%}
                             {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
                         {%- else -%}
                             {{- format_tool_response_block(ns_tname.name, tool_body) -}}
                         {%- endif -%}

                                 {%- endif -%}
                             {%- endfor -%}
                             {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
+                            {%- for part in tool_body -%}
+                                {%- if part.get('type') == 'image' -%}
+                                    {{- '<|image|>' -}}
+                                {%- elif part.get('type') == 'audio' -%}
+                                    {{- '<|audio|>' -}}
+                                {%- elif part.get('type') == 'video' -%}
+                                    {{- '<|video|>' -}}
+                                {%- endif -%}
+                            {%- endfor -%}
                         {%- else -%}
                             {{- format_tool_response_block(ns_tname.name, tool_body) -}}
                         {%- endif -%}

config.json CHANGED Viewed

@@ -72,7 +72,7 @@
     "vocab_size_per_layer_input": 262144
   },
   "tie_word_embeddings": true,
-  "transformers_version": "5.7.0",
   "video_token_id": 258884,
   "vision_config": {
     "_name_or_path": "",

     "vocab_size_per_layer_input": 262144
   },
   "tie_word_embeddings": true,
+  "transformers_version": "5.9.0",
   "video_token_id": 258884,
   "vision_config": {
     "_name_or_path": "",

generation_config.json CHANGED Viewed

@@ -10,6 +10,6 @@
   "temperature": 1.0,
   "top_k": 64,
   "top_p": 0.95,
-  "transformers_version": "5.7.0",
   "trust_remote_code": true
 }

   "temperature": 1.0,
   "top_k": 64,
   "top_p": 0.95,
+  "transformers_version": "5.9.0",
   "trust_remote_code": true
 }