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G4-MeroMero-31B
zerofata/G4-MeroMero-31B
G4-MeroMero-31B is a machine learning model from zerofata. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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At a glance
- License
- apache-2.0
- Model type
- gemma4
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- May 2, 2026
- SHA
- 35611a07
Base models
Model card
From the Hugging Face model README
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</style>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Stardom</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;900&family=JetBrains+Mono:wght@400;700&display=swap" rel="stylesheet">
</head>
<body>
<div class="gs">
<div class="gs-profile">
<div class="gs-profile-art">
<img src="https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/Mnqs466CMY930x3XzUdaQ.png" alt="image">
<div class="gs-ident">
<h1 class="gs-name">Mero Mero</h1>
<span class="gs-base">Gemma4 31B</span>
</div>
</div>
</div>
<div class="gs-section">
<div class="gs-shead">
<span class="gs-snum">01</span>
<span class="gs-stitle">Overview</span>
</div>
<div class="gs-sbody">
<p></p>
<p>A finetune of Gemma 4 31B designed for creative tasks.</p>
<p>Another difficult to work with but extremely good model from Google.</p>
<p>This model has a slightly better swipe diversity and a less flowery / verbose writing style. Reasoning tends to average out being a bit longer than the original however. Intelligence appears to be on par with the original.</p>
<p>Supports both thinking and non thinking.</p>
</div>
</div>
<div class="gs-section">
<div class="gs-shead">
<span class="gs-snum">02</span>
<span class="gs-stitle">SillyTavern Settings</span>
</div>
<div class="gs-sbody">
<div class="gs-stack">
<div class="gs-panel">
<div class="gs-panel-head">Suggested Roleplay Format</div>
<div class="gs-row"><span class="gs-key">Actions</span><span class="gs-val">In plaintext</span></div>
<div class="gs-row"><span class="gs-key">Dialogue</span><span class="gs-val">"In quotes"</span></div>
<div class="gs-row"><span class="gs-key">Thoughts</span><span class="gs-val">*In asterisks*</span></div>
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<div class="gs-panel">
<div class="gs-panel-head">Recommended Samplers</div>
<div class="gs-row"><span class="gs-key">Temp</span><span class="gs-val">0.8 - 1.0</span></div>
<div class="gs-row"><span class="gs-key">MinP</span><span class="gs-val">0.05</span></div>
<div class="gs-row"></span><span class="gs-val"></span></div>
</div>
<div class="gs-panel">
<div class="gs-panel-head">Instruct</div>
<div class="gs-row"><span class="gs-val"><a href="https://huggingface.co/zerofata/G4-MeroMero-31B/raw/main/Gemma4-Think.json">Gemma 4 - Think</a></span></div>
<div class="gs-row"><span class="gs-val"><a href="https://huggingface.co/zerofata/G4-MeroMero-31B/raw/main/Gemma4-NoThink.json">Gemma 4 - NoThink</a></span></div>
</div>
</div>
</div>
</div>
<div class="gs-section gs-section--compact">
<div class="gs-shead">
<span class="gs-snum">03</span>
<span class="gs-stitle">Quantizations</span>
</div>
<div class="gs-sbody">
<div class="gs-qrow">
<div class="gs-qpanel">
<span class="gs-qtype">GGUF</span>
<div class="gs-qsep"></div>
<a href="https://huggingface.co/zerofata/G4-MeroMero-31B-GGUF">iMatrix</a>
</div>
</div>
</div>
</div>
<div class="gs-section gs-section--journal">
<div class="gs-shead">
<span class="gs-snum">04</span>
<span class="gs-stitle">Creation Process</span>
</div>
<div class="gs-sbody">
<p>Creation Process: SFT > Merge</p>
<p>SFT on approx 49 million tokens.</p>
<p>Despite using 49 million tokens, this dataset is fairly modest in size. Trainable is somewhere in the rough ballpark of 10-15 million. All of the datasets were trained on the last turn only, to faithfully mirror the Gemma 4 chat template</p>
<p>The approach was very similar to the 26B A4B MeroMero. I trained the model aggressively for 2 epochs on my data and after testing various checkpoints, settled for the one at 1 epoch, which had the style and the least signs of overfitting.</p>
<p>I merged this checkpoint back into the original instruct which cleaned up any remaining overfitting while still retaining the changes of the finetune.</p>
<p>Trained using Axolotl.</p>
<details>
<summary>Mergekit Config</summary>
<div class="gs-detail-body">
<pre><code>models:
- model: google/gemma-4-31B-it
- model: ApocalypseParty/G4-31B-SFT-v3-1-1ep
merge_method: slerp
parameters:
t: 0.5
base_model: google/gemma-4-31B-it
dtype: bfloat16</code></pre>
</div>
</details>
<details>
<summary>Axolotl Config</summary>
<div class="gs-detail-body">
<pre><code>base_model: google/gemma-4-31B-it
 
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
- axolotl.integrations.liger.LigerPlugin
liger_layer_norm: true
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_rms_norm_gated: true
strict: false
cut_cross_entropy: true
 
datasets:
- path: zerofata/pretok
val_set_size: 0.02
output_dir: ./G4-31B-SFT-v3-1
 
sequence_len: 10756
pad_to_sequence_len: true
sample_packing: true
 
load_in_4bit: false
adapter: lora
lora_r: 64
lora_alpha: 64
peft_use_rslora: true
lora_dropout: 0.0
freeze_mm_modules: true
 
lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
 
wandb_project: G4-31B-SFT
wandb_name: G4-31B-SFT-v3-1
 
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: constant_with_warmup
learning_rate: 1e-5
max_grad_norm: 1.0
 
bf16: auto
tf32: true
 
logging_steps: 1
 
# FA2 not supported
sdp_attention: true
#flex_attention: true
#torch_compile: true
flash_attention: false
 
warmup_ratio: 0.1
evals_per_epoch: 4
saves_per_epoch: 2
weight_decay: 0.05
special_tokens:
 
fsdp_config:
fsdp_version: 2
offload_params: false
cpu_ram_efficient_loading: false
auto_wrap_policy: TRANSFORMER_BASED_WRAP
transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer
state_dict_type: FULL_STATE_DICT
sharding_strategy: FULL_SHARD
reshard_after_forward: true
activation_checkpointing: true</code></pre>
</div>
</details>
</div>
</div>
</div>
</body>
</html>
- Type
- gemma4
- License
- apache-2.0
- Created
- Apr 30, 2026
- Updated
- May 2, 2026