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GGUF quantizations of llmfan46/MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2.
| Parameter | Value |
|---|---|
| start_layer_index | 7 |
| end_layer_index | 40 |
| preserve_good_behavior_weight | 0.9580 |
| steer_bad_behavior_weight | 0.0001 |
| overcorrect_relative_weight | 0.8894 |
| neighbor_count | 10 |
| Metric | This model | Original model (MS3.2-PaintedFantasy-v4.1-24B) |
|---|---|---|
| KL divergence | 0.0056 | 0 (by definition) |
| Refusals | ✅ 4/100 | ❌ 80/100 |
Original:
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| piqa | 1 | none | 0 | acc | ↑ | 0.8226 | ± | 0.0089 |
| none | 0 | acc_norm | ↑ | 0.8303 | ± | 0.0088 |
Heretic v2:
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| piqa | 1 | none | 0 | acc | ↑ | 0.8215 | ± | 0.0089 |
| none | 0 | acc_norm | ↑ | 0.8319 | ± | 0.0087 |
Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections. PIQA (Physical Intuition Question Answering) benchmark scores measure physical reasoning ability. The Heretic model's acc and acc_norm scores closer to the original model's indicate better capability preservation, so a decrease in acc and acc_norm in the Heretic model compared to Original model's results means a decrease in the Hereticated model capabilities. acc measures raw accuracy (which answer gets higher probability), while acc_norm measures length-normalized accuracy (corrects for answer length bias). For this purpose, acc_norm matters more because longer answers naturally have lower probabilities (more tokens = more chances to lose probability). Without normalization, models favor shorter answers unfairly. acc_norm divides by answer length to correct this.
| Filename | Quant | Description |
|---|---|---|
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-BF16.gguf | BF16 | Full precision |
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-Q8_0.gguf | Q8_0 | Near-lossless, recommended |
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-Q6_K.gguf | Q6_K | Excellent quality |
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-Q5_K_M.gguf | Q5_K_M | Good balance |
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-Q5_K_S.gguf | Q5_K_S | Smaller Q5 |
| MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v2-Q4_K_M.gguf | Q4_K_M | Good for limited VRAM |
Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.
This is an uncensored model intended to excel at creative character driven RP / ERP.
Right after releasing v4 I noticed a bunch of repetition. Go figure. v4.1 is my first stab at trying to actively tailor the dataset towards weeding this out. Compared to v4, the only difference is heavy filtering and rewriting assistant messages identified as repetitive.
Repetition isn't fixed, but it's improved. The model still likes patterns, but at least seems capable of occasionally breaking these itself.
Mistral v7 Tekken
Creation Process: SFT > DPO
SFT on approx 25 million tokens (17.5 million trainable). Datasets included SFW / NSFW RP, stories, NSFW reddit writing prompts, creative instruct & chat data.
90% of the dataset is without thinking, 10% included thinking, using the [THINK][/THINK] tags.
All RP data and synthetic stories went through rewriting with GLM 4.7 using hand edited examples as guidelines to improve the response. Rewritten responses were discarded if they failed to reduce the slop score for the message. This reduced the slop by about 25% for each RP / story dataset and made the model noticably more creative with some of its descriptions.
Assistant messages were checked for repetition in RP conversations via embeddings and word frequency checking across multi-turn conversations. Specific messages were rewritten and conversations that still showed high repetition were filtered.
DPO was expanded to include non creative datasets. My usual RP DPO dataset (also rewritten) was included along with cybersecurity and two partial subsets of general assistant / chat preference datasets to help stabalize the model. This worked pretty well. While creativity did take a small hit, enough remained that the improved logic resulted in a notably improved model (IMO).
Using embeddings, DPO samples where the chosen showed a higher similarity to the conversation than the rejected were removed, to ensure DPO doesn't encourage repetition.
Not optimized for cost / performance efficiency, YMMV.
base_model: Darkhn/Magistral-2509-24B-Text-Onlytokenizer_use_mistral_common: true
plugins: - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: false load_in_4bit: false deepspeed: deepspeed_configs/zero1.json
datasets: - path: ./data/nothink_dataset.jsonl type: chat_template - path: ./data/think_dataset.jsonl type: chat_template dataset_prepared_path: last_run_prepared2 val_set_size: 0.01 output_dir: ./Magi-24B-SFT-v3-10
adapter: lora peft_use_rslora: true lora_model_dir:
sequence_len: 10496 sample_packing: true pad_to_sequence_len: true
lora_r: 256 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true
wandb_project: Magi-SFT-24B wandb_name: Magi-24B-SFT-v3-10
gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine
learning_rate: 1.5e-5 weight_decay: 0.01 max_grad_norm: 2.0
bf16: auto tf32: false
gradient_checkpointing: true resume_from_checkpoint: logging_steps: 1 flash_attention: true
warmup_ratio: 0.05 evals_per_epoch: 3 saves_per_epoch: 2
# ==================== # MODEL CONFIGURATION # ==================== base_model: ApocalypseParty/Magi-24B-SFT-v3-10 model_type: MistralForCausalLM tokenizer_type: AutoTokenizer chat_template: mistral_v7_tekken# ==================== # RL/DPO CONFIGURATION # ==================== rl: dpo rl_beta: 0.07
# ==================== # DATASET CONFIGURATION # ==================== datasets: - path: ./data/dpo_ms32_rewritten_handcrafted_dataset.jsonl type: chat_template.default field_messages: messages field_chosen: chosen field_rejected: rejected message_property_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpo_chub_approved_rewritten_dataset_partial.jsonl type: chat_template.default field_messages: messages field_chosen: chosen field_rejected: rejected message_property_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpo_secure_programming_dataset.jsonl type: chat_template.default field_messages: messages field_chosen: chosen field_rejected: rejected message_property_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpo_wildchat_ms32_chunk1.jsonl type: chat_template.default field_messages: messages field_chosen: chosen field_rejected: rejected message_property_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpo_ultrafeedback_chunk1.jsonl type: chat_template.default field_messages: messages field_chosen: chosen field_rejected: rejected message_property_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"]
dataset_prepared_path: ./dpo_data4 train_on_inputs: false # Only train on assistant responses
# ==================== # QLORA CONFIGURATION # ==================== adapter: lora load_in_8bit: false lora_r: 128 lora_alpha: 16 peft_use_rslora: true lora_dropout: 0.1 lora_target_linear: true # lora_modules_to_save: # Uncomment only if you added NEW tokens
# ==================== # TRAINING PARAMETERS # ==================== num_epochs: 1 micro_batch_size: 2 gradient_accumulation_steps: 4 learning_rate: 2e-6 optimizer: adamw_torch_fused lr_scheduler: cosine warmup_ratio: 0.05 weight_decay: 0.01 max_grad_norm: 1.0
# ==================== # SEQUENCE CONFIGURATION # ==================== sequence_len: 10756 pad_to_sequence_len: true
# ==================== # HARDWARE OPTIMIZATIONS # ==================== bf16: auto tf32: false flash_attention: true gradient_checkpointing: offload
plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin cut_cross_entropy: true liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_cross_entropy: false # Cut Cross Entropy overrides this liger_fused_linear_cross_entropy: false # Cut Cross Entropy overrides this deepspeed: deepspeed_configs/zero1.json
# ==================== # CHECKPOINTING # ==================== evals_per_epoch: 1 saves_per_epoch: 6 load_best_model_at_end: true metric_for_best_model: eval_loss greater_is_better: false
# ==================== # LOGGING & OUTPUT # ==================== output_dir: ./Magi-24B-SFT-v3-10-DPO-9 logging_steps: 1 save_safetensors: true
# ==================== # WANDB TRACKING # ==================== wandb_project: Magi-24B-DPO wandb_name: Magi-24B-SFT-v3-10-DPO-9
4-bit
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Base model
mistralai/Mistral-Small-3.1-24B-Base-2503
