TeeZee/DarkSapling-7B-v1.1

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kTool Calling:SupportedPublished:Feb 6, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

DarkSapling-7B-v1.1 by TeeZee is a 7 billion parameter language model, a merge of four Mistral-7B-based models, including Dolphin-2.6, Holodeck-1, Erebus-v3, and Samantha. This model is specifically optimized for one-on-one ERP (erotic roleplay), demonstrating a romantic and empathetic tone, seamless context switching between SFW and NSFW content, and strong character card adherence. It features a 4096-token context length and shows satisfactory storytelling capabilities.

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DarkSapling-7B-v1.1 Overview

DarkSapling-7B-v1.1 is a 7 billion parameter language model developed by TeeZee, created by merging four distinct Mistral-7B-based models: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser, KoboldAI/Mistral-7B-Holodeck-1, KoboldAI/Mistral-7B-Erebus-v3, and cognitivecomputations/samantha-mistral-7b. This merge aims to combine the strengths of its constituent models, resulting in a unique conversational agent.

Key Capabilities

  • Romantic and Empathetic Tone: The model exhibits a romantic and empathetic communication style, influenced by the Samantha model.
  • Seamless Context Switching: It can produce both SFW (safe for work) and NSFW (not safe for work) content, switching contexts smoothly without issues.
  • Character Adherence: DarkSapling-7B-v1.1 is noted for its ability to consistently stick to provided character cards.
  • Storytelling: It offers satisfactory storytelling capabilities, attributed to the Holodeck component.
  • Instruction Following: The model is proficient at following instructions.
  • Reasoning: Benefits from the underlying Mistral architecture for general intelligence, with occasional dark scenarios from Erebus.

Performance Highlights

On the Open LLM Leaderboard, DarkSapling-7B-v1.1 achieved an average score of 64.80. Notable scores include:

  • HellaSwag (10-Shot): 85.09
  • AI2 Reasoning Challenge (25-Shot): 63.48
  • MMLU (5-Shot): 64.47

Good For

  • One-on-one ERP (Erotic Roleplay): This is highlighted as its best use case due to its romantic nature and ability to handle diverse content.
  • Interactive Storytelling: Its storytelling capabilities make it suitable for narrative-driven applications.
  • Character-driven Conversations: Excels in scenarios requiring strict adherence to character profiles.