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 — 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. The checkpoint is a full Hugging Face export, not an adapter.

Recommended DJev settings:

{
  "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 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.

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.

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Safetensors

Model size

26B params

Tensor type

BF16

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