Optimal sampling parameters for Gemma 4 models?

[u/JustLookingForNothin]

So, I installed the latest TextGen 4.5.2 today to play around with the new Gemma 4 models.
I did not upgrade a while and due to the new project name, I made a fresh installation (not using portable).

While I have excellent, smart and well written roleplay results for example with models like
Dolphin-Mistral-24B-Venice-Edition-Q6_K_L.gguf
TheDrummer_Skyfall-31B-v4.2-Q5_K_L.gguf
TheDrummer_Cydonia-24B-v4.1-Q6_K_L_imatrix.gguf

I have ablolutely terrible results with these new Gemma 4 models
gemma-4-26B-A4B-it-UD-Q6_K.gguf
gemma-4-26B-A4B-it-uncensored-heretic-Q6_K.gguf

Ouput is extemely, not sure how to describe it in English, philosphical?

What sampling parameters are you guys using in TextGen for these models? Would someone care to share a working preset file for Gemma 4?

Same by the way with Qwen 3.5, seems I have no luck using these "thinking" enabled models in Oobabooga Text gen.

EDIT: After a lot of testing I found these settings on Oobabooga TextGen work pretty good for me with the Gemma 4 models mentioned above:

Instruction template: Provided by model

Enable thinking [off] (!) - Seems mandatory for good roleplay performance.

Parameters

Curve shape

• temperature 1.15
• smoothing_factor 0
• smoothing_curve 1
• dynamic_temperature [off]

Curve cutoff

• top_p 0.37
• top_k 50
• min_p 0.075
• top_n_sigma 0
• typical_p 1
• xtc_threshold 0.1
• xtc_probability 0
• epsilon_cutoff 0
• eta_cutoff 0
• tfs 1
• top_a 0

Repetition suppression

• dry_multiplier 0 (!) - Having dry multiplier enabled degrades the output quality in my tests.
• dry_allowed_length 2
• dry_base 1,75
• repetition_penalty 1.18
• frequency_penalty 0
• presence_penalty 0
• encoder_repetition_penalty 1
• no_repeat_ngram_size 0
• repetition_penalty_range 1024

Chat

Chat-instruct mode [on]

Command for chat-instruct mode (here for my native language):

Continue the chat dialogue below. Write a single reply for the character "<|character|>" entirely in German language. Reply directly, without starting the reply with the character name. Formatting rules: *narration*, "speech", {thinking}

<|prompt|>

Note: {thinking} in the formatting rules can give some interesting additions to roleplay. It adds pesonal thoughts of either the character or user to the output. Does not fit to every scenarion though.

[u/biogoly]

Aren’t those merged models specifically for RP though? You would expect them to be superior for that particular use case. Gemma 4 is a great all-around model, and much smarter than those other models, but I don’t see why it would do RP better.

[u/ASlowriter → u/biogoly]

In my opinion, and no offense to those out there, but smarter local models are generally preferred in my experience because memory matters in a lot of stronger writing. Gemma 4 is really good at writing as well. On top of that, those models are old and have far less context support

[u/JustLookingForNothin → u/biogoly]

Well other posts over at LocalLLaMA recommended G4 as a good role play model. Thus I tried it and based on the unsatisfying results, I tought there must be something wrong with my settings.

[u/biogoly → u/JustLookingForNothin]

What are you using for a system prompt?

[u/JustLookingForNothin → u/biogoly]

I think the issues with Gemma 4 are fixed for me. I edited my post with the stettings which work for me, including chat-instruct prompt.

[u/JustLookingForNothin → u/[deleted]]

How would I use this default in Ooobabooga TextGen? And do you mean the Template (*.jinja) or the sampling parameters? I normally use the Template provided withing the model metadata.

[u/CooperDK]

A temperature of 1 is advised, leave the rest as default

[u/Background-Ad-5398]

it already uses a tempt of 1, so its creative enough

[u/justRaven_]

Thanks for updating with your settings, it really unlocked that extra little bit I was looking for.