nothingiisreal/L3.1-8B-Celeste-V1.5

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Jul 27, 2024License:llama3.1Architecture:Transformer0.0K Warm

nothingiisreal/L3.1-8B-Celeste-V1.5 is an 8 billion parameter instruction-tuned causal language model based on Meta's LLaMA 3.1 8B Instruct, fine-tuned by nothingiisreal. It features an 8K context window and is specifically optimized for highly coherent and steerable creative writing and roleplaying, excelling at following OOC: instructions and adapting to diverse writing styles.

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Model Overview

L3.1-8B-Celeste-V1.5 is an 8 billion parameter model built upon Meta's LLaMA 3.1 8B Instruct, fine-tuned by nothingiisreal. It was trained with an 8K context window using a novel blend of datasets, including Reddit Writing Prompts, Kalo's Opus 25K Instruct, and c2 logs. This version prioritizes high coherency and strong adherence to OOC: (Out of Character) instructions, making it particularly steerable for creative applications.

Key Capabilities

  • Exceptional OOC: Instruction Following: The model is highly responsive to OOC: prompts, allowing users to steer its behavior and output style effectively, even deep within a conversation.
  • High Coherency and Creativity: Demonstrates increased intelligence, reduced formatting issues, and higher creativity compared to previous versions, making it suitable for complex narrative generation.
  • Flexible Writing Styles: Excels at style copying from few-shot examples and can adapt to a wide range of writing styles, including SFW and NSFW content, without explicit jailbreaks.
  • Optimized for Roleplaying: Designed to be highly engaging in roleplay scenarios, capable of evolving character personas and driving narratives proactively.

Usage Recommendations

  • System Message: A specific system message is recommended to maximize performance, focusing on character persona maintenance, narrative driving, and diverse output types.
  • Swiping and Editing: Users are encouraged to swipe 2-3 times for varied responses and to edit initial messages or use OOC: prompts to refine the model's output and steer its behavior.
  • Context Handling: While trained on 8K context, the model is expected to inherit longer context capabilities from its LLaMA 3.1 base, with testing indicating potential for up to 16K tokens.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p