Instructions to use snowicarus/diffusiongemma-26b-djev-v10-step160 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use snowicarus/diffusiongemma-26b-djev-v10-step160 with Transformers:

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

pipe = pipeline("image-text-to-text", model="snowicarus/diffusiongemma-26b-djev-v10-step160")
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("snowicarus/diffusiongemma-26b-djev-v10-step160")
model = AutoModelForMultimodalLM.from_pretrained("snowicarus/diffusiongemma-26b-djev-v10-step160", 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 snowicarus/diffusiongemma-26b-djev-v10-step160 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "snowicarus/diffusiongemma-26b-djev-v10-step160"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "snowicarus/diffusiongemma-26b-djev-v10-step160",
		"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/snowicarus/diffusiongemma-26b-djev-v10-step160

SGLang

How to use snowicarus/diffusiongemma-26b-djev-v10-step160 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 "snowicarus/diffusiongemma-26b-djev-v10-step160" \
    --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": "snowicarus/diffusiongemma-26b-djev-v10-step160",
		"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 "snowicarus/diffusiongemma-26b-djev-v10-step160" \
        --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": "snowicarus/diffusiongemma-26b-djev-v10-step160",
		"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 snowicarus/diffusiongemma-26b-djev-v10-step160 with Docker Model Runner:
```
docker model run hf.co/snowicarus/diffusiongemma-26b-djev-v10-step160
```

diffusiongemma-26b-djev-v10-step160 / README.md

snowicarus

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	---
	license: apache-2.0
	license_link: https://ai.google.dev/gemma/docs/gemma_4_license
	base_model: google/diffusiongemma-26B-A4B-it
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- diffusion-language-model
	- structured-decision-making
	- jev
	- djev
	datasets:
	- LocalLLaMA/typed-decisions
	---

	# DiffusionGemma 26B DJev v10 — step 160

	This is the selected one-step DJev checkpoint from the v10 continuation of
	`google/diffusiongemma-26B-A4B-it`. It is tuned for typed probabilistic
	decisions through the Jev/System One request schema. The selected operating
	point uses no generated reasoning block (`think=0`), four samples, and a
	64-token answer canvas.

	This is a research release. It returns calibrated decision distributions but
	should not be used as the sole decision-maker in high-impact settings.

	## Intended serving path

	The evaluated path uses the structured DiffusionGemma runtime and the
	`/v1/systemone` endpoint in the accompanying
	[Turbo-dLLM repository](https://github.com/tarsur909/Turbo-dLLM/tree/feature/djev-turnkey).
	The checkpoint is a full Hugging Face export, not an adapter.

	Recommended DJev settings:

	```json
	{
	"think": 0,
	"samples": 4,
	"canvas_size": 64
	}
	```

	The upstream `generation_config.json` remains in the export for general
	DiffusionGemma generation. The DJev scores below were measured with the
	structured one-step serving path, not generic `generate()` defaults.

	## Evaluation

	### JevBench public v1.3-format set

	All 231 public requests completed without error. This public set was reported,
	not used to select step 160; selection used held-out gates.

	\| Overall accuracy \| Easy \| Standard \| Judge \| Hard \| Hard ECE \| C13 \| I13 estimate \|
	\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| 0.8831 \| 1.000 \| 0.986 \| 0.882 \| 0.745 \| 0.044 \| 82.9 \| 83.2 \|

	In a paired run, this checkpoint exceeded `Mapika/decider-4b` v2 on the public
	set (0.8831 versus 0.8355) and on all five internal held-out sets. See the
	[comparison report](https://github.com/tarsur909/Turbo-dLLM/blob/feature/djev-turnkey/runs/decider-v2-comparison/README.md)
	for the pinned revisions, confidence intervals, latency methodology, and raw
	artifact hashes.

	### LocalLLaMA/typed-decisions test

	This result is in-task/specialist, not zero-shot: the model's training
	lineage includes the dataset's disjoint train/development rows. The 400-case
	test split was evaluation-only.

	\| Accuracy \| Soft accuracy \| KL \| TV \| Brier \| ECE \| Score MAE \| Within 1 \|
	\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| 0.687 \| 0.580 \| 0.421 \| 0.247 \| 0.166 \| 0.111 \| 0.436 \| 0.925 \|

	The exact dataset revision, test hash, scorer, per-type results, and prediction
	artifact hashes are in the
	[evaluation report](https://github.com/tarsur909/Turbo-dLLM/blob/feature/djev-turnkey/runs/typed-decisions-eval/README.md).

	## Training and selection

	- Base: `google/diffusiongemma-26B-A4B-it` at revision
	`f7f5b7f5fa82ffc52addd066915886d497f5517b`.
	- Warm start: DJev v9 step 343.
	- Update: BF16 all-text continuation with FSDP2 across eight H100s, a 4,096
	token training maximum, preservation distillation, and mixed teacher/gold
	targets.
	- Optimizer: AdamW, peak learning rate `1e-6`, cosine decay, batch size one per
	rank with four gradient-accumulation steps.
	- Candidate checkpoints: steps 160, 240, and 320. Step 160 was selected on
	held-out hard/generalization gates rather than the public score.
	- No JevBench public evaluation row or expected answer was used as training
	data. The public-development and synthetic/replay construction is recorded
	in the repository's recipes and run scripts.

	Reproduction entry points:

	- `dllm_parallel/recipes/runs/diffusiongemma-26b-djev-distill-v10.yaml`
	- `runs/data-v10/run_v10.sh`
	- `runs/decider-v2-comparison/run_comparison.sh`
	- `runs/typed-decisions-eval/evaluate_endpoint.py`

	## Limitations

	- The checkpoint is specialized for structured decisions; the fine-tune was
	not evaluated as a general chat or multimodal assistant.
	- Typed Decisions Choice and ordinal Score remain weaker than the published
	TypeSafe Jev 1.13.0 reference, while Noul accuracy is higher.
	- H100 latency measurements are serving-stack and hardware specific.
	- The model inherits the capabilities, failure modes, and license obligations
	of the DiffusionGemma base model.