Instructions to use google/diffusiongemma-26B-A4B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/diffusiongemma-26B-A4B-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/diffusiongemma-26B-A4B-it")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("google/diffusiongemma-26B-A4B-it")
model = AutoModelForMultimodalLM.from_pretrained("google/diffusiongemma-26B-A4B-it", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use google/diffusiongemma-26B-A4B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/diffusiongemma-26B-A4B-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/diffusiongemma-26B-A4B-it",
		"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/google/diffusiongemma-26B-A4B-it

SGLang

How to use google/diffusiongemma-26B-A4B-it 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 "google/diffusiongemma-26B-A4B-it" \
    --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": "google/diffusiongemma-26B-A4B-it",
		"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 "google/diffusiongemma-26B-A4B-it" \
        --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": "google/diffusiongemma-26B-A4B-it",
		"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 google/diffusiongemma-26B-A4B-it with Docker Model Runner:
```
docker model run hf.co/google/diffusiongemma-26B-A4B-it
```

Add {% generation %} markers so SFT frameworks can extract the assistant mask

#26

by CristianoLin - opened Aug 1

base: refs/heads/main

←

from: refs/pr/26

Discussion Files changed

-2

Files changed (1) hide show

chat_template.jinja +9 -2

chat_template.jinja CHANGED Viewed

@@ -346,7 +346,6 @@
             {%- endif -%}
             {%- endset -%}
-            {{- captured_content -}}
             {%- set has_content = captured_content | trim | length > 0 -%}
         {#- Forward-scan: find next non-tool message role for continuation detection -#}
@@ -365,13 +364,21 @@
             and next_nt.role == 'assistant'
             and (not message.get('tool_calls') or ns_tr_out.flag)
         ) -%}
         {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
             {{- '<|tool_response>' -}}
         {%- elif continues_into_next -%}
         {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
             {{- '<turn|>\n' -}}
         {%- endif -%}
     {%- endif -%}
     {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}

             {%- endif -%}
             {%- endset -%}
             {%- set has_content = captured_content | trim | length > 0 -%}
         {#- Forward-scan: find next non-tool message role for continuation detection -#}
             and next_nt.role == 'assistant'
             and (not message.get('tool_calls') or ns_tr_out.flag)
         ) -%}
+        {%- set captured_close -%}
         {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
             {{- '<|tool_response>' -}}
         {%- elif continues_into_next -%}
         {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
             {{- '<turn|>\n' -}}
         {%- endif -%}
+        {%- endset -%}
+        {%- if role == 'model' -%}
+            {% generation %}{{- captured_content -}}{{- captured_close -}}{% endgeneration %}
+        {%- else -%}
+            {{- captured_content -}}{{- captured_close -}}
+        {%- endif -%}
     {%- endif -%}
     {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}