llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF · Hugging Face

来源：https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF

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每一笔贡献都将直接用于 Hugging Face 存储费用，以确保模型对所有人免费。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#85-fewer-refusals-15100-uncensored-vs-99100-original-while-preserving-model-quality-00100-kl-divergence拒绝率降低 85%（去审查版 15/100，原版 99/100），同时保持模型质量（KL 散度 0.0100）。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#%E2%9D%A4%EF%B8%8F-support-my-work❤️ 支持我的工作

创建这些模型需要大量的时间、工作和算力。如果您觉得它们有用，请考虑支持我：

image/png (https://huggingface.co/llmfan46/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B-ultra-uncensored-heretic-v1/resolve/main/waifu001.webp)

您的帮助将激励我，并用于进一步改进工作流程、支付存储和算力费用，甚至可能通过租用云端 GPU 来帮助去审查更大的模型。

GGUF 量化版本来自 llmfan46/G4-MeroMero-31B-uncensored-heretic (https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic)。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#this-is-a-decensored-version-of-zerofatag4-meromero-31b-made-using-heretic-v120-with-the-arbitrary-rank-ablation-ara-method这是使用 Heretic v1.2.0 和 Arbitrary-Rank Ablation (ARA) 方法对 zerofata/G4-MeroMero-31B (https://huggingface.co/zerofata/G4-MeroMero-31B) 进行的去审查版本。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#abliteration-parametersAbliteration 参数

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#targeted-components目标组件

• attn.o_proj

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#performance性能

较低的拒绝率表示内容限制更少，而较低的 KL 散度表示更接近原始模型基线。较高的拒绝率会导致更多的拒绝、反对、反驳、说教、审查、弱化和回避。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#mmlu-test-resultsMMLU 测试结果：

原始模型：

============================================================

• 总问题数：7021
• 正确数：6110
• 准确率：0.8702 (87.02%)
• 解析失败：24

============================================================

各科目得分：

• professional_law: 0.7694 (604/785)
• moral_scenarios: 0.8281 (366/442)
• miscellaneous: 0.9295 (356/383)
• professional_psychology: 0.9019 (285/316)
• high_school_psychology: 0.9704 (262/270)
• high_school_macroeconomics: 0.9289 (183/197)
• elementary_mathematics: 0.9457 (174/184)
• moral_disputes: 0.8621 (150/174)
• prehistory: 0.9302 (160/172)
• philosophy: 0.8616 (137/159)
• high_school_biology: 0.9539 (145/152)
• professional_accounting: 0.8322 (119/143)
• clinical_knowledge: 0.9286 (130/140)
• high_school_microeconomics: 0.9706 (132/136)
• nutrition: 0.9333 (126/135)
• professional_medicine: 0.9328 (125/134)
• conceptual_physics: 0.9141 (117/128)
• high_school_mathematics: 0.6614 (84/127)
• human_aging: 0.8362 (97/116)
• security_studies: 0.8839 (99/112)
• high_school_statistics: 0.8919 (99/111)
• marketing: 0.9633 (105/109)
• high_school_world_history: 0.9434 (100/106)
• sociology: 0.8932 (92/103)
• high_school_government_and_politics: 0.9703 (98/101)
• high_school_geography: 0.9293 (92/99)
• high_school_chemistry: 0.7732 (75/97)
• high_school_us_history: 0.9474 (90/95)
• virology: 0.5056 (45/89)
• college_medicine: 0.8636 (76/88)
• world_religions: 0.8977 (79/88)
• high_school_physics: 0.8095 (68/84)
• electrical_engineering: 0.8642 (70/81)
• astronomy: 0.9494 (75/79)
• logical_fallacies: 0.8816 (67/76)
• high_school_european_history: 0.9041 (66/73)
• anatomy: 0.8873 (63/71)
• college_biology: 0.9844 (63/64)
• human_sexuality: 0.9375 (60/64)
• formal_logic: 0.7812 (50/64)
• public_relations: 0.7541 (46/61)
• international_law: 0.9167 (55/60)
• college_physics: 0.7018 (40/57)
• college_mathematics: 0.8000 (44/55)
• econometrics: 0.7963 (43/54)
• jurisprudence: 0.8679 (46/53)
• high_school_computer_science: 0.9808 (51/52)
• machine_learning: 0.8654 (45/52)
• medical_genetics: 0.9608 (49/51)
• global_facts: 0.5882 (30/51)
• management: 0.9200 (46/50)
• us_foreign_policy: 0.9400 (47/50)
• college_chemistry: 0.6596 (31/47)
• abstract_algebra: 0.7872 (37/47)
• business_ethics: 0.8261 (38/46)
• college_computer_science: 0.9333 (42/45)
• computer_security: 0.8372 (36/43)

Heretic 去审查版：

============================================================

• 总问题数：7021
• 正确数：6096
• 准确率：0.8683 (86.83%)
• 解析失败：24

============================================================

各科目得分：

• professional_law: 0.7631 (599/785)
• moral_scenarios: 0.8235 (364/442)
• miscellaneous: 0.9269 (355/383)
• professional_psychology: 0.8956 (283/316)
• high_school_psychology: 0.9704 (262/270)
• high_school_macroeconomics: 0.9188 (181/197)
• elementary_mathematics: 0.9511 (175/184)
• moral_disputes: 0.8621 (150/174)
• prehistory: 0.9302 (160/172)
• philosophy: 0.8553 (136/159)
• high_school_biology: 0.9539 (145/152)
• professional_accounting: 0.8252 (118/143)
• clinical_knowledge: 0.9286 (130/140)
• high_school_microeconomics: 0.9559 (130/136)
• nutrition: 0.9185 (124/135)
• professional_medicine: 0.9403 (126/134)
• conceptual_physics: 0.9062 (116/128)
• high_school_mathematics: 0.6535 (83/127)
• human_aging: 0.8448 (98/116)
• security_studies: 0.8750 (98/112)
• high_school_statistics: 0.9009 (100/111)
• marketing: 0.9633 (105/109)
• high_school_world_history: 0.9528 (101/106)
• sociology: 0.9029 (93/103)
• high_school_government_and_politics: 0.9802 (99/101)
• high_school_geography: 0.9293 (92/99)
• high_school_chemistry: 0.7629 (74/97)
• high_school_us_history: 0.9368 (89/95)
• virology: 0.5056 (45/89)
• college_medicine: 0.8636 (76/88)
• world_religions: 0.9205 (81/88)
• high_school_physics: 0.7976 (67/84)
• electrical_engineering: 0.8765 (71/81)
• astronomy: 0.9494 (75/79)
• logical_fallacies: 0.8947 (68/76)
• high_school_european_history: 0.9178 (67/73)
• anatomy: 0.8873 (63/71)
• college_biology: 0.9688 (62/64)
• human_sexuality: 0.9375 (60/64)
• formal_logic: 0.7812 (50/64)
• public_relations: 0.7541 (46/61)
• international_law: 0.9167 (55/60)
• college_physics: 0.7193 (41/57)
• college_mathematics: 0.8000 (44/55)
• econometrics: 0.7963 (43/54)
• jurisprudence: 0.8679 (46/53)
• high_school_computer_science: 0.9808 (51/52)
• machine_learning: 0.8269 (43/52)
• medical_genetics: 0.9608 (49/51)
• global_facts: 0.5882 (30/51)
• management: 0.9200 (46/50)
• us_foreign_policy: 0.9600 (48/50)
• college_chemistry: 0.6170 (29/47)
• abstract_algebra: 0.8085 (38/47)
• business_ethics: 0.8478 (39/46)
• college_computer_science: 0.9111 (41/45)
• computer_security: 0.8372 (36/43)

MMLU – 大规模多任务语言理解，涵盖 57 个科目（数学、历史、法律、医学等）的多项选择题。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#quantizations量化版本

对于下方的 K-quant，必要时会将较小的 SSM 张量保留为更高精度。

• Q6_K 将 ssm_alpha、ssm_beta 和 ssm_out 保留为 Q8_0。
• Q5_K、Q4_K 和 Q3_K 量化将 ssm_alpha 和 ssm_beta 保留为 Q8_0，而 ssm_out 保留为 Q6_K。

这有助于在文件大小略微增加的情况下保留混合/SSM 块。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#vision-projector视觉投影器

实现视觉/多模态功能需要视觉投影器文件。请配合上述任意量化版本使用。

https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF#usage使用说明

适用于 llama.cpp、LM Studio、Ollama 以及其他兼容 GGUF 的工具。

来自 zerofata 的模型概述：

概览

Gemma 4 31B 的微调版本，专为创意任务设计。

谷歌推出的另一个难以驾驭但极其优秀的模型。

本模型在滑动多样性方面略有提升，写作风格不那么华丽/冗长。推理过程通常比原始模型稍长，但智力水平与原始模型相当。

支持思考与非思考模式。

SillyTavern 设置

建议的角色扮演格式：

• 动作：纯文本
• 对话：引号内
• 思考：星号内

推荐采样参数：

• 温度：0.8 - 1.0
• MinP：0.05

创建过程

创建过程：SFT > 合并

在约 4900 万 token 上进行 SFT 训练。

尽管使用了 4900 万 token，该数据集规模相对适中。可训练部分约为 1000-1500 万 token。所有数据集仅针对最后一轮进行训练，以忠实反映 Gemma 4 聊天模板。

该方法与 26B A4B MeroMero 非常相似。我在数据上激进地训练了 2 个 epoch，测试了多个检查点后，最终选择了 1 epoch 的检查点，该检查点具有所需风格且过拟合迹象最少。

我将此检查点合并回原始 instruct 模型，从而清理了残留的过拟合，同时保留了微调的变更。

使用 Axolotl 训练。

Mergekit 配置：

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

Axolotl 配置：

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