cgato/Thespis-CurtainCall-7b-v0.3

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Mar 2, 2024License:cc-by-nc-4.0Architecture:Transformer0.0K Open Weights Cold

Thespis-CurtainCall-7b-v0.3 by cgato is a 7 billion parameter language model with an 8192 token context length. It is fine-tuned on a diverse array of datasets including Dolphin, Ultrachat, Capybara, and Airoboros 3.1, with specific inclusion of datasets like ToxicQA and grimulkan/physical-reasoning. This model is designed for chat-based interactions, excelling in conversational applications and scenarios requiring nuanced understanding from varied data sources.

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Model Overview

The cgato/Thespis-CurtainCall-7b-v0.3 is a 7 billion parameter language model developed by cgato, featuring an 8192 token context window. This iteration is a fine-tuned model, building upon a diverse set of training data to enhance its conversational and reasoning capabilities.

Key Training Datasets

The model was trained on a comprehensive collection of datasets, indicating a focus on broad conversational understanding and specific reasoning tasks. Notable datasets include:

  • Dolphin, Ultrachat, Capybara, Airoboros 3.1, OpenOrca: These contribute to general conversational fluency and instruction following.
  • Augmental, Yahoo Answers: Likely enhance its ability to process and generate informative responses.
  • ToxicQA: Suggests an effort to handle or understand potentially sensitive or challenging content.
  • Magiccoder-Evol-Instruct-110k: Indicates some exposure to code-related instructions or logical reasoning.
  • grimulkan/physical-reasoning and theory-of-mind: These datasets are particularly interesting, pointing towards an emphasis on understanding physical interactions and inferring mental states, which can be crucial for advanced conversational agents and role-playing scenarios.

Recommended Usage

The model is optimized for chat-based interactions and follows a standard chat prompt format. It is compatible with popular interfaces like Oobabooga and Silly Tavern. Recommended settings for optimal performance in Silly Tavern include a Temperature of 1.25, MinP of 0.1, and Repetition Penalty of 1.03.

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