RP Chat
Ratings reflect this model as run on Arli AI.
Gemma-4-31B-Harmonia 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 ModelsInput (24H)
0
Output (24H)
0
Input (7D)
0
Output (7D)
0
Typical Total Time
30.47s
Typical TTFT
7.48s
Typical Input TPS
1661.81
Typical Output TPS
14.72
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
10/31/2026
Gemma-4-31B-Harmonia is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Harmonia 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-Harmonia",
"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-Harmonia",
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.
base_model:
Gemini Word Salad Initialization
Harmonia is a high-dimensional 31-billion parameter merge of Gemma 4. By executing a meticulous three-phase fusion of seven elite foundation and specialized models, Harmonia demonstrates a targeted approach to deep neural consolidation, minimizing regression while amplifying unique capability boundaries.
Instead of simple linear blending, which often degrades logical coherence and dilutes nuanced behavior, Harmonia was sculpted using a combination of mathematical projections, covariance activation matching, and surgical synaptic pruning. The model appears pretty solid so far.
The lineage of Harmonia is constructed systematically, passing through three isolated mathematical states to layer capabilities cleanly.
To anchor base capabilities, the primary Gemma-4-31B-Base is combined with the analytically rigorous GarnetV2-31B. Utilizing low-rank Singular Value Decomposition (SVD), the specialized donor features are projected entirely onto the mathematical null-space of the base weights. This prevents the creative delta vectors from distorting essential core intelligence, producing the stable platform clever-basename.
Next, our newly anchored base is layered with the highly independent cognitive engines MeroMero-31B and Gembrain-31B. We apply Context-Aware Binary Selection (CABS) to execute structured, localized parameter gating. By enforcing precise structural pruning ratios (retaining optimal synapses in 16:32 and 11:33 ratios), we weave complex creative reasoning directly into the core matrix without causing neural interference. The result is the highly expressive clever-intname.
In the final harmonization phase, the expressive clever-intname is combined with the narrative mastery of Equinox-31B, the creative depth of Fabled-Gemma4, and our primary conversational core Ortenzya-The-Creative-Wordsmith. Using data-free covariance estimation via task vectors, ACTMat reconstructs layer-wise input activation properties, solving for optimal projection weights in activation space. This resolves semantic alignment anomalies and delivers the unified output model.
We extend our gratitude to the creators of the ancestral paths that intersect within Harmonia:
The entire orchestration sequence is structured via a multi-stage MergeKit pipeline. Expand the block below to view the structural YAML recipes.
name: clever-basename
merge_method: nullspace
base_model: ./gemma-4-31B-base
models:
- model: ./Gemma4-GarnetV2-31B
parameters:
weight: 1.0
parameters:
protect_base: true
nr: 256
tokenizer:
source: base
chat_template: auto
dtype: float32
out_dtype: bfloat16
---
name: clever-intname
merge_method: cabs
base_model: ./clever-basename
models:
- model: ./clever-basename
- model: ./G4-MeroMero-31B-uncensored-heretic
parameters:
weight: 0.6
n_val: 16
m_val: 32
- model: ./Gemma-4-Gembrain-31B-heretic
parameters:
weight: 0.4
n_val: 11
m_val: 33
default_n_val: 8
default_m_val: 32
pruning_order:
- ./G4-MeroMero-31B-uncensored-heretic
- ./Gemma-4-Gembrain-31B-heretic
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
chat_template: auto
---
name: Harmonia
merge_method: actmat
base_model: ./gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic
models:
- model: ./gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic
- model: ./LatitudeGames-Equinox-31B
parameters:
weight: 1
- model: ./clever-intname
parameters:
weight: 1
- model: ./Fabled-Gemma4-31B
parameters:
weight: 1
parameters:
epsilon: 1e-6
tokenizer:
source: "union"
dtype: bfloat16
out_dtype: bfloat16
chat_template: auto
I am grateful to the following individuals for their models, inspiration, and other contributions.:
And of course, every wonderful person on:
LocalLLaMAA big thanks to Gemini-3.5-flash for creating this README alongside the word salads found within it. A special acknowledgment is extended to Google DeepMind for their contribution of the Gemma-4 foundation family to the open-weight ecosystem, representing the structural cornerstone of this merge and its constituents.