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README.md
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base_model:
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- unsloth/mistral-7b-v0.3
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library_name: transformers
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tags:
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---
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# asstr-supernova
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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###
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base_model: unsloth/mistral-7b-v0.3+/home/mango/Trainers/outputs/checkpoint-1053
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dtype: bfloat16
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merge_method: passthrough
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models:
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- model: unsloth/mistral-7b-v0.3+/home/mango/Trainers/outputs/checkpoint-1053
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```
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---
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tags:
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- roleplay
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- storywriting
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- mistral
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- finetune
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datasets:
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- NewEden/Orion-Asstr-Stories-16K
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: mistralai/Mistral-7B-v0.3
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Tags:
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- Chat
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---
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A finetune of Mistral-7B-V0.3 to test out the Orion-Asstr dataset, This model was completion trained with Orion Asstr using Unsloth.
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*THIS MODEL HAS NOT BEEN TRAINING WITH INSTRUCT DATA, IT IS NOT MEANT FOR CHAT, FOR THAT CHECK OUT OUT THE INSTRUCT TUNED VERSION [HERE](https://huggingface.co/Delta-Vector/Hamanasu-7B-instruct/)
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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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```py
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from trl import SFTTrainer
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from transformers import TrainingArguments
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from unsloth import is_bfloat16_supported
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from unsloth import UnslothTrainer, UnslothTrainingArguments
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trainer = UnslothTrainer(
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model = model,
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tokenizer = tokenizer,
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train_dataset = dataset,
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dataset_text_field = "text",
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max_seq_length = max_seq_length,
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dataset_num_proc = 8,
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args = UnslothTrainingArguments(
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per_device_train_batch_size = 2,
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gradient_accumulation_steps = 8,
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warmup_ratio = 0.1,
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num_train_epochs = 1,
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learning_rate = 1e-5,
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embedding_learning_rate = 1e-5,
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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 = "paged_adamw_8bit",
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weight_decay = 0.00,
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lr_scheduler_type = "cosine",
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seed = 3407,
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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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```
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</details><br>
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## Credits
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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)
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## Training
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The training was done for 1 epoch. We used 1 x RTX A4000
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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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