An experimental finetune based on the Llama3.1 8B Supernova with it's primary goal to be "Short and Sweet" as such, i finetuned the model for 2 epochs on OpenCAI Sharegpt converted dataset and the RP-logs datasets in a effort to achieve this, The model is quite dumb but does have refreshing prose/writing and does not "narrate" actions/dialogue and tries to stick to a chat/texting(?) format. # Quants GGUF: https://huggingface.co/Delta-Vector/Control-8B-gguf EXL2 (Thanks Lucy <3) : https://huggingface.co/Delta-Vector/Control-8B-EXL2 ## 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

See axolotl config

Axolotl version: `0.4.1` ```yaml base_model: arcee-ai/Llama-3.1-SuperNova-Lite model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/CharacterAI-logs-sharegpt-Ngram-Cleaned type: sharegpt conversation: llama3 - path: NewEden/OpenCAI-ShareGPT type: sharegpt conversation: llama3 chat_template: llama3 #val_set_size: 0.01 output_dir: ./outputs adapter: lora_r: lora_alpha: lora_dropout: lora_target_linear: sequence_len: 16384 # sequence_len: 32768 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true wandb_project: CAI-Supernova wandb_entity: wandb_watch: wandb_name: CAI-Supernova-2 wandb_log_model: plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true gradient_accumulation_steps: 2 micro_batch_size: 1 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 weight_decay: 0.05 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: unsloth early_stopping_patience: resume_from_checkpoint: #auto_resume_from_checkpoints: true local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 15 #evals_per_epoch: 4 eval_table_size: #eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_params.json 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), [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 2 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. [

](https://github.com/OpenAccess-AI-Collective/axolotl) ## Safety Nein.