Create README.md

README.md

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+---
+license: mit
+base_model:
+- zerofata/G4-MeroMero-26B-A4B
+---
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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/xBv_weuMs5x3i4WFDRksn.png" alt="image">
+<div class="gs-ident">
+<h1 class="gs-name">Mero Mero</h1>
+<span class="gs-base">Gemma4 26B A4B</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>God, this model was difficult to work with.</p>
+<p>Google cooked, there wasn't a lot to improve but there was a lot to break.</p>
+<p>This model is a finetune that was merged back into the original instruct. It feels a lot like the original instruct. However, reasoning is more structured, using less tokens during RP and this model generally has a slightly less verbose / flowery writing style.</p>
+<p>Main weakness of this model I think is the swipe variety hasn't improved. Logic and repetition I think are roughly 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-26B-A4B/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-26B-A4B/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-26B-A4B-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 35 million tokens.</p>
+<p>Despite using 35 million tokens, this dataset is fairly modest in size. Trainable is somewhere in the rough ballpark of 15 million. The extra tokens are from a new multi turn RP dataset that I train last turn only.</p>
+<p>Feels like Google left the instruct model at the razor's edge of overfitting. Finetune it at all and it feels like it'll rapidly lose intelligence, despite taking the writing style nicely. Hard to tell if you're overfitting or underfitting.</p>
+<p>My solution was to blast the model with my data anyway to ensure it picked up the new reasoning format and writing style and then merge that back into the instruct to heal the logic damage. There's still room for a better merge that keeps more of the writing style and potentially using the base model to undo some of the overfitting.</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;26B&#45;A4B&#45;it
+    parameters:
+      weight: 0.5
+  &#45; model: ApocalypseParty/G4&#45;26B&#45;SFT&#45;6
+    parameters:
+      weight: 0.5
+merge_method: linear
+dtype: bfloat16</code></pre>
+</div>
+</details>
+<details>
+<summary>Axolotl Config</summary>
+<div class="gs-detail-body">
+<pre><code>&#35; Gemma 4 26B&#45;A4B MoE QLoRA with ScatterMoE kernels
+&#35;
+&#35; Validated: 50 steps on FineTome&#45;100k, loss 8.8 &#45;> 1.8, single RTX 5090 (32GB)
+&#35; torch_compile=true: 21 GiB peak VRAM, ~230 tok/s, 336s total
+&#35;
+&#35; Key notes:
+&#35; &#45; Max sequence length on 32GB GPU: 2048 (micro_batch_size=1, SDP attention).
+&#35;   4096 seq_len OOMs due to head_dim=512 math SDP materializing full score matrix.
+&#35;   Use 48GB+ GPUs for longer sequences or multi&#45;GPU with FSDP.
+&#32;
+base_model: google/gemma&#45;4&#45;26B&#45;A4B&#45;it
+&#32;
+plugins:
+  &#45; axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  &#45; axolotl.integrations.kernels.KernelsPlugin
+  &#45; axolotl.integrations.liger.LigerPlugin
+use_kernels: true
+use_scattermoe: true
+cut_cross_entropy: true
+experts_implementation: scattermoe
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+strict: false
+&#32;
+datasets:
+  &#45; path: ./data/gemma_4_sft_5_masked_20260415_082234.jsonl
+val_set_size: 0.02
+output_dir: ./G4&#45;26B&#45;SFT&#45;6
+&#32;
+sequence_len: 10756
+pad_to_sequence_len: true
+sample_packing: true
+&#32;
+load_in_4bit: false
+&#35;quantize_moe_experts: true
+adapter: lora
+lora_r: 128
+lora_alpha: 128
+peft_use_rslora: true
+lora_dropout: 0.0
+freeze_mm_modules: true
+&#32;
+&#35; Restrict LoRA to text backbone only (skip vision/audio encoders)
+&#35; using regex to match only the text decoder attention projections.
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+&#32;
+&#35; MoE expert LoRA (3D Parameter tensors, not nn.Linear)
+lora_target_parameters:
+  &#45; experts.gate_up_proj
+  &#45; experts.down_proj
+&#32;
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+&#32;
+&#35;bnb_config_kwargs:
+&#35;  bnb_4bit_use_double_quant: true
+&#32;
+wandb_project: G4&#45;26B&#45;SFT
+wandb_name: G4&#45;26B&#45;SFT&#45;6
+&#32;
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+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;
+&#35;gradient_checkpointing: true
+&#35;activation_offloading: true
+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: 4
+weight_decay: 0.01
+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>