Sao10K/L3-8B-Stheno-v3.2

Hugging Face
TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jun 5, 2024License:cc-by-nc-4.0Architecture:Transformer0.5K Open Weights Featherless Exclusive Warm

Sao10K/L3-8B-Stheno-v3.2 is an 8 billion parameter language model developed by Sao10K, fine-tuned from a Llama-3 base. This iteration, Stheno-v3.2-Zeta, is optimized for balanced SFW/NSFW storywriting and narration, alongside improved assistant-style tasks. It features enhanced multi-turn coherency and better adherence to prompts and instructions, making it suitable for diverse conversational and creative generation applications.

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Model Overview

Sao10K/L3-8B-Stheno-v3.2 is an 8 billion parameter model, fine-tuned by Sao10K from a Llama-3 base. This version, Stheno-v3.2-Zeta, represents the sixth iteration of development, focusing on refining its generative capabilities across various tasks. The training involved approximately 24 hours on a single H100 SXM GPU, with hyperparameter tuning to achieve lower loss levels.

Key Enhancements & Capabilities

  • Balanced Content Generation: Improved handling of both SFW (Safe For Work) and NSFW (Not Safe For Work) content, achieving a more balanced output compared to previous versions.
  • Enhanced Storywriting & Narration: Demonstrates better performance in generating coherent and engaging stories and narrative passages.
  • Improved Assistant-Style Tasks: More effective at handling instruction-based and assistant-type queries.
  • Multi-Turn Coherency: Features better consistency and reduced issues in multi-turn conversations, maintaining context more effectively.
  • Prompt Adherence: Shows stronger adherence to given prompts and instructions, leading to more predictable and controlled outputs.

Training Data & Methodology

This version incorporated a mix of SFW and NSFW storywriting data, including contributions from Gryphe's Opus-WritingPrompts dataset. It also included more instruct/assistant-style data and underwent further cleaning of roleplaying samples to remove low-quality entries. While slightly less creative than its predecessor, this is considered a worthwhile trade-off for improved control and coherency.

Recommended Usage

Users are advised to use specific sampling parameters for optimal performance:

  • Temperature: 1.12-1.22
  • Min-P: 0.075
  • Top-K: 50
  • Repetition Penalty: 1.1

The model utilizes the Llama-3-Instruct prompting template and includes recommended stopping strings for effective interaction.

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