--- tags: - chat datasets: - NewEden/OpenCAI-ShareGPT - NewEden/vanilla-backrooms-claude-sharegpt - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/Roleplay-Logs-V2 Language: - En Pipeline_tag: text-generation Base_model: mistralai/Mistral-Nemo-Instruct-2407 Tags: - Chat --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/7F2mX4Qqzmp8b0KG5i2DM.png) ### exl2 quant (measurement.json in main branch) --- ### check revisions for quants A finetune of Mistral-Nemo-Instruct-2407 using conversational data, aiming for prose that's best described as 'short' and 'sweet.' The model strictly adheres to one-on-one roleplay and is very dialogue heavy. # Quants GGUF: [Placeholder till Mradermacher quants.] EXL2 : https://huggingface.co/Delta-Vector/Ohashi-NeMo-12B-EXL2 ## Prompting Model has been tuned with the Mistral formatting. A typical input would look like this: ```py [INST] SYSTEM MESSAGE USER MESSAGE[/INST] ASSISTANT MESSAGE[INST] USER MESSAGE[/INST] ``` ## System Prompting I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.
See Sao10k's Euryale System Prompt ``` Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. Follow the instructions in , avoiding the items listed in . ```

See EVA System Prompt ``` A fictional, narrative-driven role-play emphasizing versatility and UNCENSORED content. Adherence to the `Role-playing Guidelines` is mandatory. Refer to the `Role-play Context` for accurate information.\n\n\n ### Narration Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact. Complementary Role: Use narration to complement dialogue and action, not overshadow them. Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action. ### Narrative Consistency Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative. ### Character Embodiment Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'. Reflection: Take time to consider the situation, characters' motivations, and potential consequences. Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals.

### Narration Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact. Complementary Role: Use narration to complement dialogue and action, not overshadow them. Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action. ### Narrative Consistency Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative. ### Character Embodiment Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'. Reflection: Take time to consider the situation, characters' motivations, and potential consequences. Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals. ", ```
## Axolotl config
See axolotl config Axolotl version: ` 0.5.0` ```yaml base_model: mistralai_Mistral-Nemo-Instruct-2407 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/OpenCAI-ShareGPT type: chat_template # chat_template: mistralv3tekken roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn - path: NewEden/vanilla-backrooms-claude-sharegpt type: chat_template # chat_template: mistralv3tekken roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn - path: anthracite-org/kalo_opus_misc_240827 type: chat_template # chat_template: mistralv3tekken roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn - path: anthracite-org/kalo_misc_part2 type: chat_template # chat_template: mistralv3tekken roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn - path: NewEden/Roleplay-Logs-V2 type: chat_template # chat_template: mistralv3tekken roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn dataset_prepared_path: dataset_prepared val_set_size: 0.0 output_dir: 12b-out-r2 sequence_len: 16384 sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 128 lora_alpha: 16 lora_dropout: 0.05 #lora_target_linear: #lora_fan_in_fan_out: true peft_use_rslora: true lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: 12b-control wandb_entity: wandb_watch: wandb_name: 12b-control-r2 wandb_log_model: gradient_accumulation_steps: 2 micro_batch_size: 1 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.00001 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: unsloth #gradient_checkpointing_kwargs: # use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 40 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 1 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.03 fsdp: fsdp_config: special_tokens: pad_token: ```

## Credits Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [Intervitens](https://huggingface.co/intervitens), [Tav](https://huggingface.co/tavtav), [Trappu](https://huggingface.co/Trappu), [Cgato](https://huggingface.co/cgato), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) ## Training The training was done for 4 epochs. We used 4 x [RTX 3090s](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/) GPUs graciously provided by [Intervitens](https://huggingface.co/intervitens) for the fine-tuning of the model. [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ## Safety ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/Wjgbdd-YjB56SCgrjdtL3.png)