--- license: agpl-3.0 tags: - chat datasets: - NewEden/OpenCAI-ShareGPT - NewEden/Roleplay-Logs-Sharegpt-Ngram-cleaned - HuggingFaceH4/ultrafeedback_binarized License: agpl-3.0 Language: - En Pipeline_tag: text-generation Base_model: arcee-ai/Llama-3.1-SuperNova-Lite Tags: - Chat --- --- ### GGUF quants, EXL2 can be found in the link below --- 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, This version of Control has been finetuned with DPO to help improve the smart's and coherency which was a flaw i noticed in the previous model. # Quants GGUF: https://huggingface.co/Delta-Vector/Control-8B-V1.1-GGUF/ EXL2 (Thanks Lucy <3) : https://huggingface.co/Delta-Vector/Control-8B-V1.1-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. ", ```
## Unsloth config
See Unsloth Trainer config ```yaml dpo_trainer = DPOTrainer( model = model, ref_model = None, args = DPOConfig( per_device_train_batch_size = 1, gradient_accumulation_steps = 8, warmup_ratio = 0.1, num_train_epochs = 2, learning_rate = 5e-6, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.02, lr_scheduler_type = "linear", seed = 42, output_dir = "outputs", report_to = "none", # Use this for WandB etc ), beta = 0.1, train_dataset = raw_datasets["train"], # eval_dataset = raw_datasets["test"], tokenizer = tokenizer, max_length = 1024, max_prompt_length = 512, ) ```

## Credits Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [CelineDion](https://huggingface.co/CelineDion), [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) (But not Alpin.) ## 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, After which DPO tuning was on 1 x [Nvidia T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/) [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) [Made with Unsloth](https://github.com/unslothai/unsloth) ## Safety Nein.