Median Total Time
121.76s
Median TTFT
67.93s
Median Prefill TPS
1.12
Median Gen TPS
8.79
Context Size
262144
Quantization
r64
Engine
vllm
Creation Method
LoRA Finetune
Model Type
Gemma31B
Chat Template
Gemma4
Reasoning
Yes
Vision
Yes
Parameters
31B
Added At
7/19/2026
license: apache-2.0 language:

A fun, experimental model merge that aims for more creative prose and less stereotypical portrayal of characters, without losing smartness and prompt adherence, compared to its base model google/gemma-4-31b-it.
This model was put together as a fun project between Ateron and Nimbz, the trope heavily inspired by the visual of a humming, glowing and colorful "Gemsicle" crafted for everyone in our little Blazed Forge.
We put our heads together because we both liked the EQ and overall intelligence of the base Gemma 4 31B it model, but we had to acknowledge that it is full of slop phrases, stereotypical character display and narrow paths with almost no swipe variety. The released fine-tunes seemed promising, but they made evident that there is a trade-off between training the base model for more creativity and better prose and preserving the overall intelligence of the model.
We tried to bring both of these worlds together - Preserve the strengths of the base and enhance it with the creative capabilities of our selection of sophisticated fine-tunes.
Please refer to the original google/gemma-4-31b-it for the correct chat template.
Let your frontend handle the chat template if possible (e.g., Chat Completion in SillyTavern).
<|think|> at the very beginning of the system prompt. Thinking happens between <|channel>thought\n and
<channel|> tags.<|turn>system
<|think|>
You are a helpful assistant<turn|>
<|turn>user
Hello<turn|>
<|turn>model
Hi there<turn|>
<|turn>user
How are you?<turn|>
<|turn>model
These are the settings we found work best to keep the model creative without it getting too repetitive or losing the plot. Feel free to experiment, as a different behavior might be desireable for others.
| Settings | With Adaptive-P | Without Adaptive-P |
|---|---|---|
| Temperature | 1.0 | 1.0 |
| Top-K | 0 | 0 |
| Top-P | 0.97 | 0.90 |
| Min-P | 0.03 | 0.03 |
| Adaptive-P Target | 0.6 | - |
| Adaptive-P Decay | 0.5 | - |
ST Chat Completion Parameters:
temperature: 1.0
top_k: 0
top_p: 0.97
min_p: 0.03
adaptive_target: 0.6
adaptive_decay: 0.5
samplers: ["top_k", "top_p", "min_p", "temperature", "adaptive_p"]
chat_template_kwargs: {"thinking": true, "enable_thinking": true}
We used mergekit to combine the strengths of several different models in a precise, two-phase metallurgical process:
First, we took AuriAetherwiing/G4-31B-Musica-v1 and ConicCat/Gemma4-GarnetV2-31B - two highly creative fine-tunes - and merged them together in an even 50:50 approach with Spherical Linear Interpolation (SLERP). This served as the creative portion for the better wording and swipe variety we were seeking for, creating the Symphony-v2 merge.
models:
- model: ./Gemma4-GarnetV2-31B
- model: ./G4-31B-Musica-v1
merge_method: slerp
base_model: ./Gemma4-GarnetV2-31B
parameters:
t:
- value: 0.5
dtype: bfloat16
In the second step, we injected specific weights from a randomly found, but somehow very capable fine-tune called p-e-r-e-g-r-i-n-e/Sprinkle-Gemma-4-31B, which inherits enough of the intelligence and prompt ahderence known from the base model, while bringing in enough variable but realistic prose and character portrayal to be of use.
Using the DARE-TIES method, we merged the Sprinkle-Gemma-4-31B with the Symphony-v1 and created our gem with the qualities we sought for.
models:
- model: ./gemma-4-31B-it
- model: ./Sprinkle-Gemma-4-31B
parameters:
density: [0.90, 0.80, 0.60, 0.65]
weight: [0.25, 0.30, 0.70, 0.60]
- model: ./Symphony-v2
parameters:
density: [0.05, 0.10, 0.20, 0.35]
weight: [0.0, 0.0, 0.30, 0.50]
merge_method: dare_ties
base_model: ./gemma-4-31B-it
parameters:
int8_mask: true
dtype: bfloat16
| Quant | Size | Download Link |
|---|---|---|
| Q4_K_S | 17.8 GB | Click |
| Q4_K_M | 18.7 GB | Click |
| Q5_K_S | 21.3 GB | Click |
| Q5_K_M | 21.8 GB | Click |
| Q6_K | 25.2 GB | Click |
| Q8_0 | 32.6 GB | Click |
A huge thanks to the creators of the community tools and open-source models that made this possible!