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Gemma-4-31B-Schattenblume

RP Chat

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Ratings reflect this model as run on Arli AI.

Gemma-4-31B-Schattenblume is a text generation model available on Arli AI. It offers 31B parameters, 262K token context, reasoning support, vision input. Recommended use: RP Chat. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.

View on Hugging FaceBack to Models

Hourly Usage

Token Usage

Input (24H)

63K

Output (24H)

83

Input (7D)

2M

Output (7D)

17K

Performance Metrics

Typical Total Time

17.40s

Typical TTFT

2.77s

Typical Input TPS

1722.97

Typical Output TPS

17.45

Model Information

Context Size

262144

Quantization

r128 on FP8

Engine

vllm

Creation Method

Merge

Model Class

Gemma-4-31B - LoRA

Chat Template

Gemma4

Reasoning

Yes

Vision

Yes

Parameters

31B

Added At

9/26/2026

Use Gemma-4-31B-Schattenblume with the Arli AI API

Gemma-4-31B-Schattenblume is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Schattenblume as the model, and requests are served without logging.

cURL

curl https://api.arliai.com/v1/chat/completions \
  -H "Authorization: Bearer $ARLIAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Gemma-4-31B-Schattenblume",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.arliai.com/v1",
    api_key="YOUR_ARLIAI_API_KEY",
)

response = client.chat.completions.create(
    model="Gemma-4-31B-Schattenblume",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

See the text generation docs for every parameter, or read the quick start guide to make your first request.

Similar models

license: apache-2.0 base_model:

  • google/gemma-4-31B-it
  • ReadyArt/gemma-4-31B-it-scotoma-2
  • Blazed-Forge/Gemma-4-Giftige-Blume-31B-v1
  • zerofata/G4-MeroMero-v2-31B base_model_relation: merge tags:
  • merge
  • mergekit
  • reasoning
  • non-reasoning
  • creative writing
  • roleplay
  • conversational
  • character-rp
  • storytelling
  • low-refusal
  • nsfw
  • 31B
  • gemma-4

SCHATTENBLUME

31B · Gemma 4 · Merge
"It grows where the light doesn't."
THREE MODELS
LESS GEMMAISM
SWIPE VARIETY
IN-CHARACTER ADHERENCE
ROLEPLAY MERGE
LOW REFUSALS

❀ About The Model

Meet Schattenblume-31B, the shadow flower. Leaning on the nightshade, which is Nachtschatten in German. Nightshade is the kind of pretty that doesn't warn you about anything, because it isn't hiding anything either. The berries are glossy and they taste sweet. That's not a trap, that's just what they are. I guess your parents warned you not to eat them.
Right at the beginning it should be pointed out that this one is made purely because I was asked to and it seemed like more than just one person wanted it, so here we are - initially not my idea, I just made it work. I can only guess that they tested these three models - scotoma-2, Giftige-Blume-v1, MeroMero-v2 - and said they were the good ones for prose and creativity. So the brief was simple: take scotoma-2, Giftige-Blume-v1 and MeroMero-v2, and play all three of them to their strengths. Basically, that's the whole thing - three ingredients this time, plus the base, and one single della_linear phase.
Before any confusion comes up: This one is roleplay first, actually just like Froopert could be used for RP of *any* kind, but let's just make it blunt this time. I think it's not just the system prompt I'm using; you should be able to expect a model that plays characters closer to how they're written than to how you'd like them to be mid-chat, thanks to the Giftige-Blume, and it will push back if it's fitting. If you want a model that says yes to everything, this isn't it (or you better prompt for it).

❀ GGUF Quants

Thank you to anyone providing quants! Links land here as they show up.
⬛ STATIC ◆ IMATRIX (soon)

☾ The Merge

One della_linear pass over the gemma-4-31B-it base, run on zerofata's gemma-4-support fork of mergekit.
ModelPurposeImpacted Layers
scotoma-2kills the Gemma tics, holds adherence and format at the output end0-59 · 0.30 → 0.62 by depth
dips to 0.20 mid on purpose
spine (every 6th) 0.80
Giftige-Blume v1character portrayal, scene, imagery, the actual prose0-59 minus spine · peak 0.56 mid
MeroMero v2swipe variety, entity tracking, action-forward writing0-59 minus spine · peak 0.30 front-mid
down to 0.08 by 59

◆ Some things I paid attention to in this merge:

I tried not to blend blindly, but to hit the specific layers where each model shines. Every merge I've done before spread the donors evenly and let the weights sort it out. This time the three curves cross each other on purpose. Blume goes up where the character work happens and scotoma-2 goes down in the same place, then they swap over the back half so the corrector owns the output. MeroMero sits high early and steps out at the end, because the variety belongs where all the character tracking happens. Ultimately it was a design decision, for better or worse, I didn't measure it, just tested it and rolled with it because it seemed to work.
It was a bit of a fight to get variety & adherence right. I've found that two of three had reports of being a bit loose on instruction following in their threats, so scotoma-2 is the beeg strong one here. So scotoma-2 comes up for the last layers and the spine, and the two flavour models are pinned to zero on every sixth layer, like usual. If you crunch the numbers you'll see that it's cooked slightly hotter than Froopert, but embeddings, output head, final norm, layer scalars and the vision tower are all filtered out, so everything should be intact.
View recipe - della_linear 
merge_method: della_linear
base_model: ./google/gemma-4-31B-it
dtype: bfloat16
tokenizer_source: base
parameters:
  normalize: false
  epsilon: 0.05
  lambda: 1.0
  int8_mask: true

models:

  - model: ./ReadyArt/gemma-4-31B-it-scotoma-2
    parameters:
      density: 0.85
      weight:
        - { filter: embed_tokens, value: 0.0 }
        - { filter: lm_head, value: 0.0 }
        - { filter: embed_vision, value: 0.0 }
        - { filter: vision_tower, value: 0.0 }
        - { filter: multi_modal_projector, value: 0.0 }
        - { filter: model.language_model.norm., value: 0.0 }
        - { filter: layer_scalar, value: 0.0 }
        - { filter: model.language_model.layers.5.,  value: 0.80 }
        - { filter: model.language_model.layers.11., value: 0.80 }
        - { filter: model.language_model.layers.17., value: 0.80 }
        - { filter: model.language_model.layers.23., value: 0.80 }
        - { filter: model.language_model.layers.29., value: 0.80 }
        - { filter: model.language_model.layers.35., value: 0.80 }
        - { filter: model.language_model.layers.41., value: 0.80 }
        - { filter: model.language_model.layers.47., value: 0.80 }
        - { filter: model.language_model.layers.53., value: 0.80 }
        - { filter: model.language_model.layers.59., value: 0.80 }
        - value: [0.30, 0.26, 0.20, 0.38, 0.62]

  - model: ./Blazed-Forge/Gemma-4-Giftige-Blume-31B-v1
    parameters:
      density: 0.55
      weight:
        - { filter: embed_tokens, value: 0.0 }
        - { filter: lm_head, value: 0.0 }
        - { filter: embed_vision, value: 0.0 }
        - { filter: vision_tower, value: 0.0 }
        - { filter: multi_modal_projector, value: 0.0 }
        - { filter: model.language_model.norm., value: 0.0 }
        - { filter: layer_scalar, value: 0.0 }
        - { filter: model.language_model.layers.5.,  value: 0.0 }
        - { filter: model.language_model.layers.11., value: 0.0 }
        - { filter: model.language_model.layers.17., value: 0.0 }
        - { filter: model.language_model.layers.23., value: 0.0 }
        - { filter: model.language_model.layers.29., value: 0.0 }
        - { filter: model.language_model.layers.35., value: 0.0 }
        - { filter: model.language_model.layers.41., value: 0.0 }
        - { filter: model.language_model.layers.47., value: 0.0 }
        - { filter: model.language_model.layers.53., value: 0.0 }
        - { filter: model.language_model.layers.59., value: 0.0 }
        - value: [0.30, 0.42, 0.56, 0.42, 0.30]

  - model: ./zerofata/G4-MeroMero-v2-31B
    parameters:
      density: 0.60
      weight:
        - { filter: embed_tokens, value: 0.0 }
        - { filter: lm_head, value: 0.0 }
        - { filter: embed_vision, value: 0.0 }
        - { filter: vision_tower, value: 0.0 }
        - { filter: multi_modal_projector, value: 0.0 }
        - { filter: model.language_model.norm., value: 0.0 }
        - { filter: layer_scalar, value: 0.0 }
        - { filter: model.language_model.layers.5.,  value: 0.0 }
        - { filter: model.language_model.layers.11., value: 0.0 }
        - { filter: model.language_model.layers.17., value: 0.0 }
        - { filter: model.language_model.layers.23., value: 0.0 }
        - { filter: model.language_model.layers.29., value: 0.0 }
        - { filter: model.language_model.layers.35., value: 0.0 }
        - { filter: model.language_model.layers.41., value: 0.0 }
        - { filter: model.language_model.layers.47., value: 0.0 }
        - { filter: model.language_model.layers.53., value: 0.0 }
        - { filter: model.language_model.layers.59., value: 0.0 }
        - value: [0.24, 0.30, 0.30, 0.22, 0.08]

❀ Sampler Recommendations

Feel free to check, or don't. Samplers are weird black magic with Gemma.
View sampler settings
Setting
Value
Temperature
0.8 - 1.1
Top-K
0
Top-P
0.95
Min-P
0.05 - 0.15
Repetition Penalty
off
Adaptive-P Target (optional)
0.6
Adaptive-P Decay (optional)
0.5

☾ Usage

◆  Thinking. Add <|think|> at the very start of the system prompt to enable reasoning, otherwise let your front-end handle the Gemma 4 template if possible (e.g., Chat Completion in SillyTavern). Both rows work. If your scenario is token-heavy, definitely turn it on.
◆  If swipes feel same-y, touch the samplers before you blame the merge. Variety should be handled thanks to MeroMero-v2 quite nicely, but I could only let it carry as much weight as it can without dragging the prose back toward stock Gemma. So, try to up Temperature and Min-P if you need it.
◆  Give it a scene to work with. It really pays off with a card that has a place and a mood in it already. So try to give it something to chew on to let it unfold its full glory.

❀ Credits & Special Thanks

  • AesSedai: What shall I say? The second merge in a row built on scotoma-2. It's still the best answer anyone has to the Gemma voice. Kudos once again.
  • zerofata: Once for MeroMero v2 for the swipe variety and the character stability, and twice for the gemma-4-support fork of mergekit that every single merge of mine runs on. Also for being straight about what v2 trades away, it really helped to narrow things down.
  • ReadyArt: Thanks for the cool fine-tunes and for letting me hang around!
  • Mergekit: Thanks to ArceeAI for mergekit.
  • The BeaverAI Community: This merge is literally a community recommendation from Nesy and Sytan. They named these three models, and before Nesy will start talking about it over and over, I turned it into a damn recipe. But honestly, things like that are the reason this is weirdly the best room on the internet for this hobby.
  • Ateron: The rat guy that cursed me with the habit of merging. Thank you Mr. Rat.
  • Google DeepMind & The Open-Source Community: for the base model, for the fine-tunes that keep getting better, and for everyone whose work ends up inside these things eventually.
❀ ◦ ☾ ◦ ❀