# DiffusionGemma Grammar and Word-Merging Issues in Question Generation

> Source: <https://discuss.huggingface.co/t/diffusiongemma-grammar-and-word-merging-issues-in-question-generation/178788#post_1>
> Published: 2026-08-18 08:49:57+00:00

Hi everyone,

I am currently running **RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic** using vLLM through Docker.

My current vLLM command is:

```
vllm serve --model RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic \
  --trust-remote-code \
  --attention-backend TRITON_ATTN \
  --max-num-seqs 4 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.78 \
  --generation-config vllm \
  --hf-overrides '{"diffusion_sampler":"entropy_bound","diffusion_entropy_bound":0.1}' \
  --diffusion-config '{"canvas_length":256}' \
  --host 0.0.0.0 \
  --port 8085
```

I am using the same model/server in two different applications:

Interestingly, the model works reasonably well in the RAG chatbot. I don’t see noticeable grammar problems or word-merging issues there.

However, in the **marine-domain question-generation application**, I sometimes get outputs with:

For example, I can get output similar to:

`upcomingbunkering operation`

instead of:

`upcoming bunkering operation`

I also see cases where the generated question structure or grammar is not correct.

The important part is that **the same model and vLLM server are being used**, but the problem is much more noticeable in the question-generation workflow.

I understand that DiffusionGemma is experimental and that its output quality may not be comparable to standard autoregressive Gemma models. However, I am trying to understand whether the behavior I’m seeing is actually a model limitation or whether there is something different in my question-generation pipeline that I am missing.

Could the difference be caused by things such as:

`canvas_length=256`

`entropy_bound`

/ `diffusion_entropy_bound`

Has anyone experienced similar **word-merging or grammar issues with DiffusionGemma specifically during question generation or structured output generation**?

Any suggestions on what I should compare or test between the RAG and question-generation pipelines would be appreciated. I would particularly like to know whether this is expected behavior from the diffusion generation approach or whether there is a configuration issue I should investigate.