![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/6L-SXxQZ2nxYwvIjnlzN8.png) *Nanuqsaurus, a polar tyrannosaur, was a cold-adapted apex predator that prowled the Arctic during the Cretaceous, hunting what dared live in the cold nights* A fine-tuned version of LLaMA 3.1 8B Supernova, designed to be "short and sweet" by minimizing narration and lengthy responses. It was fine-tuned over 4 epochs using OpenCAI and RP logs, with DPO applied to enhance coherence. Finally—thanks to Jeiku—we implemented KTO reinforcement learning, significantly improving the model's prose and creativity. # Quants GGUF: https://huggingface.co/Delta-Vector/Control-Nanuq-8B-GGUF EXL2 (Thanks Lucy <3) : https://huggingface.co/Delta-Vector/Control-Nanuq-8B ## Prompting Model has been tuned with the LLama-Instruct formatting. A typical input would look like this: ```py """<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an AI built to rid the world of bonds and journeys!<|eot_id|><|start_header_id|>user<|end_header_id|> Bro i just wanna know what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> """ ``` *Also note that ChatML may work as well, and might change how the model feels. while still being coherent and stable* ## 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 *For previous configs such as the base Axolotl finetune/DPO trainer config, Refer back to the older version of Control*

See Axolotl KTO Trainer config

```yaml base_model: Delta-Vector/Control-8B-V1.1 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false hub_model_id: jeiku/controlkto hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true chat_template: llama3 rl: kto rl_beta: 0.2 kto_desirable_weight: 0.2 datasets: - path: NewEden/full-opus-chosen-hermes-rejected-kto-v1-merged type: llama3.argilla shuffle_merged_datasets: true val_set_size: 0.0 output_dir: ./outputs/out adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 64 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: sequence_len: 8192 sample_packing: false eval_sample_packing: false pad_to_sequence_len: false wandb_project: controlkto wandb_entity: wandb_watch: wandb_name: controlkto wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 2 num_epochs: 2 max_steps: 500 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 0.0001 weight_decay: 0.05 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true remove_unused_columns: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 2 eval_table_size: eval_max_new_tokens: saves_per_epoch: 1 debug: deepspeed: fsdp: fsdp_config: fsdp: fsdp_config: special_tokens: pad_token: <|finetune_right_pad_id|> eos_token: <|eot_id|> ```

## Credits Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [jeiku](https://huggingface.co/jeiku), [Intervitens](https://huggingface.co/intervitens), [Kalomaze](https://huggingface.co/kalomaze), [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 full-parameter fine-tuning of the model, DPO tuning was on 1 x [Nvidia T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/) and finally KTO was perforaned with 1 x [H100](https://www.nvidia.com/en-us/data-center/h100/) GPU graciosuly provided by jeiku [

](https://github.com/OpenAccess-AI-Collective/axolotl) [

](https://github.com/unslothai/unsloth) ## Safety Nein.