zerofata/G4-MeroMero-v2-31B-GGUF overview

---

license: apache-2.0

base_model:

• zerofata/G4-MeroMero-v2-31B

---

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<html lang="en">

<head>

<meta charset="UTF-8">

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<div class="gs-profile">

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<div class="gs-ident">

<h1 class="gs-name">Mero Mero v2</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, particularly narrative RP. Intended to be a more creative version of <a href="https://huggingface.co/zerofata/G4-MeroMero-31B">G4-MeroMero-31B</a>.</p>

<p>This model is the result of a lot of experimentation and learning. Trying to make Gemma 4 more creative without destroying the intelligence is... difficult. To put it mildly.</p>

<p style="margin-top:16px">Heavily inspired by a few research papers, <a href="https://arxiv.org/abs/2604.03136">StoryScope: Investigating idiosyncrasies in AI fiction

</a> and particularly <a href="https://arxiv.org/abs/2605.26492">Elias in the Lighthouse, Again?</a>. Measuring these narrative tics and attractors against simple prompts seems to be a good way to target the model's slop and kick start giving Gemma 4 some diversity: anything that repeatedly occurs across generations of such a generic prompt is something the model is overusing.</p>

<p>Compared to the original, swipes are notably more diverse and feel less like Gemma. RP slop is measurably lower (at least for the type of slop I measure). IFEval / GSM8K / MMLU-Pro are the same as stock with no obvious degradation. The only intelligence drop I've really noticed so far is when you get a swipe that goes a bit hot.</p>

<p>Supports both thinking and non thinking. Reasoning averages longer than stock Gemma 4, but shorter than MeroMero v1.</p>

</div>

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<div class="gs-section">

<div class="gs-shead">

<span class="gs-snum">02</span>

<span class="gs-stitle">SillyTavern Settings</span>

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<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>

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<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-v2-31B/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-v2-31B/raw/main/Gemma4-NoThink.json">Gemma 4 - NoThink</a></span></div>

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<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-v2-31B-GGUF">iMatrix</a>

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<div class="gs-section">

<div class="gs-shead">

<span class="gs-snum">04</span>

<span class="gs-stitle">Evaluation</span>

</div>

<div class="gs-sbody">

<table class="gs-etable">

<tr><th></th><th class="gs-ecol">Mero Mero v2</th><th>Mero Mero v1</th><th>Stock Gemma 4</th></tr>

<tr class="gs-egroup"><td colspan="4">Swipe diversity &mdash; given an RP conversation, generate 8 swipes and evaluate how varied the beats in those swipes are, GLM-judged with a rubric.</td></tr>

<tr><td>Thinking off</td><td class="gs-best">0.72</td><td>0.57</td><td>0.43</td></tr>

<tr><td>Thinking on</td><td class="gs-best">0.62</td><td>0.49</td><td>0.32</td></tr>

<tr class="gs-egroup"><td colspan="4">Slop &amp; attractors &mdash; lower is better</td></tr>

<tr><td>Slop per 1k words, RP replies</td><td class="gs-best">15.5</td><td>18.0</td><td>18.5</td></tr>

<tr><td>Slop per 1k words, stories</td><td class="gs-best">7.4</td><td>8.3</td><td>8.8</td></tr>

<tr><td>Bare-prompt stories hitting an attractor</td><td class="gs-best">66%</td><td>98%</td><td>99%</td></tr>

<tr class="gs-egroup"><td colspan="4">Top attractor markers &mdash; each model's six most frequent, stories containing each of 144</td></tr>

<tr><td>#1</td><td>Tuesday &middot; 28</td><td>Elias &middot; 96</td><td>Elias &middot; 102</td></tr>

<tr><td>#2</td><td>Arthur &middot; 20</td><td>Tuesday &middot; 81</td><td>Tuesday &middot; 90</td></tr>

<tr><td>#3</td><td>Elias &middot; 19</td><td>Clara &middot; 57</td><td>Clara &middot; 80</td></tr>

<tr><td>#4</td><td>Leo &middot; 16</td><td>Oakhaven &middot; 46</td><td>Oakhaven &middot; 60</td></tr>

<tr><td>#5</td><td>Elara &middot; 14</td><td>Arthur &middot; 21</td><td>Thorne &middot; 23</td></tr>

<tr><td>#6</td><td>Clara &middot; 14</td><td>Leo &middot; 20</td><td>Arthur &middot; 16</td></tr>

<tr class="gs-egroup"><td colspan="4">Thinking length &mdash; words per think block, RP replies; shorter is better</td></tr>

<tr><td>Mean / median</td><td>341 / 305</td><td>382 / 342</td><td class="gs-best">263 / 253</td></tr>

<tr class="gs-egroup"><td colspan="4">General benchmarks &mdash; thinking off; IFEval &amp; GSM8K full, MMLU-Pro 40q per category</td></tr>

<tr><td>IFEval</td><td class="gs-best">90.2</td><td>89.8</td><td>89.8</td></tr>

<tr><td>GSM8K</td><td class="gs-best">97.0</td><td>96.1</td><td>96.7</td></tr>

<tr><td>MMLU-Pro</td><td class="gs-best">85.5</td><td>85.4</td><td>84.6</td></tr>

</table>

</div>

</div>

<div class="gs-section gs-section--journal">

<div class="gs-shead">

<span class="gs-snum">05</span>

<span class="gs-stitle">Creation Process</span>

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<div class="gs-sbody">

<p>Creation Process: SFT > Merge > GRPO > GRPO > on-policy SFT</p>

<p><strong>Stage 1 — Diversity SFT.</strong> Stock Gemma 4 collapses hard on underspecified creative prompts ("Write a story." basically always gives clockmaker or memory related stories in a shop with Elias). Trained on ~4,000 short stories curated against the storyscope narrative prompts and found attractors. The dataset is a mix of human stories and synthetic stories from a set of frontier models, with diverse generation prompts swapped out for generic ones and filtered for quality. I also included some of the usual creative instruct and roleplay data. The model came out alright. Creative, but notably worse at instruction following with degraded intelligence. SLERP-merged back into the original instruct at t=0.5, which basically reverted it to stock Gemma 4 with slightly improved prose and creativity (similar to MeroMero).</p>

<p><strong>Stage 2 — Creative GRPO (with think disabled).</strong> TRL GRPO (via Axolotl), 8 rollouts per prompt on the same bare prompts. Reward stack: LLM-judge diversity and coherence rewards, an attractor-marker penalty seeded from stock and then updated with whatever started appearing as new attractors during training, narrative-rate penalties and deterministic degeneracy guards (checking for non-Latin characters, joined words etc). 300 steps.</p>

<p><strong>Stage 3 — RP logic GRPO (with think enabled).</strong> 100 further steps on multi-turn roleplay contexts: a thinking check to ensure it always parsed correctly, a logic-defect judge (DeepSeek-V4 Flash with a rubric), per-context attractor lists mined from k=8 baselines of the stage 2 model, and the same degeneracy checks as stage 2.</p>

<p><strong>Stage 4 — On-policy multi-outcome RP SFT.</strong> ~3,300 samples the model wrote itself on roleplay contexts, filtered to keep only varied continuations. The model wasn't able to produce diverse results purely on-policy, so a natural response was generated first, then each sample went through a GLM-5.2 critique pipeline: error detection, plus steering ideas injected as OOC commands for re-generation (DeepSeek-V4-Pro / GLM-5.2 alternating). Everything was then filtered for errors, slop and any degeneracy as usual. Roughly 60% of samples include thinking. Last turn only.</p>

<p>Trained using Axolotl.</p>

<details>

<summary>Stage 1 — Diversity SFT Config (Axolotl)</summary>

<div class="gs-detail-body">

<pre><code>base_model: google/gemma&#45;4&#45;31B&#45;it

&#32;

plugins:

&#45; axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

&#45; 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

&#32;

datasets:

&#45; path: ./data/diversity_sft_masked.jsonl

val_set_size: 0

output_dir: ./G4&#45;31B&#45;SFT&#45;v10&#45;2

&#32;

sequence_len: 8192

pad_to_sequence_len: true

sample_packing: true

&#32;

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'

&#32;

gradient_accumulation_steps: 1

micro_batch_size: 4

num_epochs: 2

optimizer: adamw_torch_fused

lr_scheduler: cosine

learning_rate: 1e&#45;5

max_grad_norm: 1.0

warmup_ratio: 0.1

weight_decay: 0.05

saves_per_epoch: 2

&#32;

bf16: auto

tf32: true

&#32;

&#35; FA2 not supported

sdp_attention: true

flash_attention: false

&#32;

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>

<details>

<summary>Stage 1 — Mergekit Config</summary>

<div class="gs-detail-body">

<pre><code>merge_method: slerp

base_model: google/gemma&#45;4&#45;31B&#45;it

models:

&#45; model: google/gemma&#45;4&#45;31B&#45;it

&#45; model: ApocalypseParty/G4&#45;31B&#45;SFT&#45;v10&#45;2

parameters:

t: 0.5

dtype: bfloat16</code></pre>

</div>

</details>

<details>

<summary>Stage 2 — Creative GRPO Config (Axolotl)</summary>

<div class="gs-detail-body">

<pre><code>base_model: /workspace/models/configCA &#35; stage 1 output

&#32;

rl: grpo

&#32;

trl:

reward_funcs:

&#45; rewards_g4.reward_judge_diversity

&#45; rewards_g4.reward_judge_coherence

&#45; rewards_g4.reward_attractor

&#45; rewards_g4.reward_narrative

&#45; rewards_g4.reward_sane

reward_weights: [3.0, 3.0, 0.75, 1.0, 1.0]

beta: 0.02

num_generations: 8

max_completion_length: 1600

temperature: 1.0

use_vllm: true

scale_rewards: true

loss_type: grpo

epsilon: 0.2

generation_kwargs:

stop_token_ids: [1, 106, 50]

top_k: 64

top_p: 0.95

&#32;

datasets:

&#45; path: /workspace/data/sft_train_final.jsonl

type: ebft_chat.transform

&#32;

sequence_len: 2048

micro_batch_size: 2

gradient_accumulation_steps: 4

max_steps: 200

&#32;

learning_rate: 4.0e&#45;6

optimizer: adamw_torch_fused

lr_scheduler: cosine

warmup_steps: 10

weight_decay: 0.01

&#32;

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'

&#32;

max_grad_norm: 1.0

bf16: auto

tf32: true

sdp_attention: true

flash_attention: false

gradient_checkpointing: true

&#32;

&#35; shipped weights use checkpoint&#45;100 of this run</code></pre>

</div>

</details>

<details>

<summary>Stage 3 — RP Logic GRPO Config (Axolotl)</summary>

<div class="gs-detail-body">

<pre><code>base_model: /workspace/models/r4b100 &#35; stage 2 output

&#32;

rl: grpo

&#32;

trl:

reward_funcs:

&#45; rewards_rp.reward_thinking &#35; format gate on the think block

&#45; rewards_rp.reward_logic &#35; constraint&#45;grounded defect judge

&#45; rewards_rp.reward_attractor &#35; frozen per&#45;context lists from stock k=8

&#45; rewards_rp.reward_sane &#35; deterministic glitch guards

reward_weights: [2.0, 3.0, 1.0, 1.0]

beta: 0.02

num_generations: 8

max_completion_length: 2560

temperature: 1.0

use_vllm: true

scale_rewards: true

loss_type: grpo

epsilon: 0.2

generation_kwargs:

stop_token_ids: [1, 106, 50]

top_k: 64

top_p: 0.95

&#32;

datasets:

&#45; path: /workspace/rp/rp3_train.jsonl

type: ebft_chat.transform

&#32;

sequence_len: 8192

micro_batch_size: 1

gradient_accumulation_steps: 8

max_steps: 100

&#32;

learning_rate: 3.0e&#45;6

optimizer: adamw_torch_fused

lr_scheduler: cosine

warmup_steps: 10

weight_decay: 0.01

&#32;

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'

&#32;

max_grad_norm: 1.0

bf16: auto

tf32: true

sdp_attention: true

flash_attention: false

gradient_checkpointing: true</code></pre>

</div>

</details>

<details>

<summary>Stage 4 — On-Policy Multi-Outcome SFT Config (Axolotl)</summary>

<div class="gs-detail-body">

<pre><code>base_model: ApocalypseParty/G4&#45;31B&#45;r4b100&#45;GRPO&#45;rp100 &#35; stage 3 output

&#32;

plugins:

&#45; axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

&#45; 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

&#32;

datasets:

&#45; path: ./data/g4_onpolicy_rp_masked.jsonl

val_set_size: 0

output_dir: ./G4&#45;31B&#45;r4b100&#45;GRPO&#45;rp100&#45;sft

&#32;

sequence_len: 8192

pad_to_sequence_len: true

sample_packing: true

&#32;

adapter: lora

lora_r: 64

lora_alpha: 64

peft_use_rslora: false

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'

&#32;

gradient_accumulation_steps: 2

micro_batch_size: 1

num_epochs: 1

optimizer: adamw_torch_fused

lr_scheduler: cosine

learning_rate: 4e&#45;5

max_grad_norm: 1.0

warmup_ratio: 0.1

weight_decay: 0.05

saves_per_epoch: 2

&#32;

bf16: auto

tf32: true

&#32;

&#35; FA2 not supported

sdp_attention: true

flash_attention: false

&#32;

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>

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