Instructions to use google/diffusiongemma-26B-A4B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# 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=40) 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
fix: chat_template, close model turn across tool results + keep tool-call reasoning
Browse files### Summary
Two defects in `chat_template.jinja`, both verified with [ChatLint](https://github.com/dimondevceo/ChatLint) against 49 conversation shapes. The patched file is attached / inlined as this PR.
| | Hub `main` | this PR |
| --- | --- | --- |
| score | 260/294 (88%) | **283/294 (96%)** |
| errors | **25** | **2** |
The remaining 2 are the preamble-hoist issue also present on E4B/31B (assistant text emitted after `<tool_response|>`); not addressed here.
### Fix 1: misplaced `endif` (22 `marker_balance` errors)
`ns.prev_non_tool_role` was assigned **outside** the `role != 'tool'` block, so a tool message overwrote it to `'tool'`. The next assistant turn then failed `continue_same_model_turn` and opened a second `<|turn>model` without closing the first:
```
broken: ...<tool_response|><|turn>model\nIt is 18C...
fixed: ...<tool_response|>It is 18C...<turn|>\n<|turn>user\n
```
This is the same class of turn-tag imbalance the Jul 15 fix cleared on `gemma-4-31B-it` / `gemma-4-E4B-it`. Those repos already have the `endif` in the right place; Diffusion Gemma did not get that change.
```diff
{%- endif -%}
- {%- endif -%}
-
{#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}
{%- set ns.prev_non_tool_role = message['role'] -%}
+ {%- endif -%}
{%- endfor -%}
```
### Fix 2: tool-call reasoning dropped after the next user turn
```diff
- {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or (preserve_thinking and message.get('tool_calls')) -%}
+ {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or message.get('tool_calls') or preserve_thinking -%}
```
With the default `preserve_thinking=false`, an assistant turn that called a tool lost its `reasoning_content` as soon as a later user message existed, blowing ~27% of the cached prefix on every agent step after the first tool call.
### Reproduce
```bash
pip install chatlint
# before
curl -sL https://huggingface.co/google/diffusiongemma-26B-A4B-it/raw/main/chat_template.jinja \
| chatlint check /dev/stdin
# after (this PR's file)
chatlint check chat_template.jinja
```
### Ask
If this lands, consider adding [ChatLint's CI workflow](https://github.com/dimondevceo/ChatLint/blob/main/.github/workflows/chatlint.yml) so a sibling repo cannot drift from the Jul 15 fix again. The original Gemma 4 turn-tag regression survived 96 days; this file shows the same class of bug can reappear on a fork that nobody re-gated.
- chat_template.jinja +2 -2
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@@ -237,7 +237,7 @@
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{#- Render reasoning/reasoning_content as thinking channel -#}
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{%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
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-
{%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or
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{%- if thinking_text and thinking_gate -%}
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{{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
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{%- endif -%}
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@@ -372,10 +372,10 @@
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{%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
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{{- '<turn|>\n' -}}
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{%- endif -%}
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-
{%- endif -%}
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{#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}
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{%- set ns.prev_non_tool_role = message['role'] -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{#- Render reasoning/reasoning_content as thinking channel -#}
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{%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
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+
{%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or message.get('tool_calls') or preserve_thinking -%}
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{%- if thinking_text and thinking_gate -%}
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{{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
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{%- endif -%}
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{%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
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{{- '<turn|>\n' -}}
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{%- endif -%}
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{#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}
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{%- set ns.prev_non_tool_role = message['role'] -%}
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+
{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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