Delta-Vector
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---
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license: agpl-3.0
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tags:
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- chat
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datasets:
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- NewEden/OpenCAI-ShareGPT
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- NewEden/Roleplay-Logs-Sharegpt-Ngram-cleaned
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- HuggingFaceH4/ultrafeedback_binarized
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License: agpl-3.0
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: arcee-ai/Llama-3.1-SuperNova-Lite
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Tags:
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- Chat
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---
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---
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### exl2 quant (measurement.json in main branch)
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---
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### check revisions for quants
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---
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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.
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# Quants
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GGUF: https://huggingface.co/Delta-Vector/Control-8B-V1.1-GGUF/
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EXL2 (Thanks Lucy <3) : https://huggingface.co/Delta-Vector/Control-8B-V1.1-EXL2
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## Prompting
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Model has been tuned with the LLama-Instruct formatting. A typical input would look like this:
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```py
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"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are an AI built to rid the world of bonds and journeys!<|eot_id|><|start_header_id|>user<|end_header_id|>
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Bro i just wanna know what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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```
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*Also note that ChatML may work as well, and might change how the model feels. while still being coherent and stable*
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## System Prompting
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I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.
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<details><summary>See Sao10k's Euryale System Prompt</summary>
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```
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Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.
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<Guidelines>
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• Maintain the character persona but allow it to evolve with the story.
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• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.
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• All types of outputs are encouraged; respond accordingly to the narrative.
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• Include dialogues, actions, and thoughts in each response.
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• Utilize all five senses to describe scenarios within {{char}}'s dialogue.
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• Use emotional symbols such as "!" and "~" in appropriate contexts.
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• Incorporate onomatopoeia when suitable.
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• Allow time for {{user}} to respond with their own input, respecting their agency.
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• Act as secondary characters and NPCs as needed, and remove them when appropriate.
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• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.
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</Guidelines>
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<Forbidden>
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• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.
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• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.
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• Repetitive and monotonous outputs.
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• Positivity bias in your replies.
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• Being overly extreme or NSFW when the narrative context is inappropriate.
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</Forbidden>
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Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.
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```
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</details><br>
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<details><summary>See EVA System Prompt</summary>
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```
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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
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<!-- Start of Role-playing Guidelines -->
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### Narration
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Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact.
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Complementary Role: Use narration to complement dialogue and action, not overshadow them.
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Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action.
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### Narrative Consistency
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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.
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### Character Embodiment
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Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'.
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Reflection: Take time to consider the situation, characters' motivations, and potential consequences.
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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.
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<!-- End of Role-playing Guidelines -->
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</details><br>
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### Narration
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Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact.
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Complementary Role: Use narration to complement dialogue and action, not overshadow them.
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Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action.
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### Narrative Consistency
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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.
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### Character Embodiment
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Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'.
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Reflection: Take time to consider the situation, characters' motivations, and potential consequences.
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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.
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<!-- End of Role-playing Guidelines -->",
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```
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</details><br>
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## Unsloth config
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<details><summary>See Unsloth Trainer config</summary>
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```yaml
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dpo_trainer = DPOTrainer(
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model = model,
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ref_model = None,
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args = DPOConfig(
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per_device_train_batch_size = 1,
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gradient_accumulation_steps = 8,
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warmup_ratio = 0.1,
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num_train_epochs = 2,
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learning_rate = 5e-6,
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fp16 = not is_bfloat16_supported(),
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bf16 = is_bfloat16_supported(),
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logging_steps = 1,
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optim = "adamw_8bit",
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weight_decay = 0.02,
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lr_scheduler_type = "linear",
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seed = 42,
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output_dir = "outputs",
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report_to = "none", # Use this for WandB etc
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),
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beta = 0.1,
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train_dataset = raw_datasets["train"],
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# eval_dataset = raw_datasets["test"],
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tokenizer = tokenizer,
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max_length = 1024,
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max_prompt_length = 512,
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)
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```
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</details><br>
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## Credits
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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.)
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## Training
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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/)
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" alt="Made with Unsloth" width="200" height="32"/>](https://github.com/unslothai/unsloth)
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## Safety
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Nein.
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