G4-MeroMero-31B-mlx-4bit-gs32

beezu/G4-MeroMero-31B-mlx-4bit-gs32

This model beezu/G4-MeroMero-31B-mlx-4bit-gs32 was converted to MLX format from zerofata/G4-MeroMero-31B using mlx-lm version 0.31.3.

About this quant

Uses group size of 32.

Original Model Card

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font-size: 0.9rem; } .gs-cfg-title { font-family: var(--mono); font-size: 0.72rem; font-weight: 700; letter-spacing: 0.1em; text-transform: uppercase; color: var(--dim); margin: 0 0 8px; } /* ── Code ── */ .gs pre { background: #f0f4f8; border: 1px solid var(--edge); border-left: 2px solid var(--azure); padding: 16px 18px; overflow-x: auto; font-family: var(--mono); font-size: 0.76rem; line-height: 1.6; color: var(--text); margin: 0 0 22px; } .gs pre:last-child { margin-bottom: 0; } .gs pre code { background: none; color: inherit; padding: 0; } .gs code { font-family: var(--mono); font-size: 0.875em; color: var(--crimson); background: var(--az-glow); padding: 2px 5px; } </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> </div> <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 24B 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: &#45; model: google/gemma&#45;4&#45;31B&#45;it &#45; model: ApocalypseParty/G4&#45;31B&#45;SFT&#45;v3&#45;1&#45;1ep merge_method: slerp parameters: t: 0.5 base_model: google/gemma&#45;4&#45;31B&#45;it dtype: bfloat16</code></pre> </div> </details> <details> <summary>Axolotl Config</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: zerofata/prek_qwen36 val_set_size: 0.02 output_dir: ./G4&#45;31B&#45;SFT&#45;v3&#45;1 &#32; sequence_len: 10756 pad_to_sequence_len: true sample_packing: true &#32; load_in_4bit: false adapter: lora lora_r: 64 lora_alpha: 64 peft_use_rslora: true lora_dropout: 0.0 freeze_mm_modules: true &#32; lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj' &#32; wandb_project: G4&#45;31B&#45;SFT wandb_name: G4&#45;31B&#45;SFT&#45;v3&#45;1 &#32; gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 2 optimizer: adamw_torch_fused lr_scheduler: constant_with_warmup learning_rate: 1e&#45;5 max_grad_norm: 1.0 &#32; bf16: auto tf32: true &#32; logging_steps: 1 &#32; &#35; FA2 not supported sdp_attention: true &#35;flex_attention: true &#35;torch_compile: true flash_attention: false &#32; warmup_ratio: 0.1 evals_per_epoch: 4 saves_per_epoch: 2 weight_decay: 0.05 special_tokens: &#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> </body> </html>