cgato/Thespis-Krangled-7b-v2

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Mar 14, 2024License:cc-by-nc-4.0Architecture:Transformer0.0K Open Weights Cold

cgato/Thespis-Krangled-7b-v2 is a 7 billion parameter language model developed by cgato, fine-tuned on a diverse array of datasets including Dolphin, Ultrachat, and Magiccoder-Evol-Instruct-110k. This model is designed for general conversational AI and instruction-following tasks, leveraging its broad training data to handle varied prompts. With an 8192-token context length, it offers robust performance for interactive applications.

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Overview

cgato/Thespis-Krangled-7b-v2 is a 7 billion parameter language model, distinguished by its extensive fine-tuning on a wide variety of datasets. These include general conversational datasets like Dolphin, Ultrachat, and Capybara, as well as specialized datasets such as Magiccoder-Evol-Instruct-110k for code-related instructions, and ToxicQA for handling nuanced content. The model supports a context length of 8192 tokens, making it suitable for maintaining longer conversations and processing more extensive inputs.

Key Capabilities

  • Broad Instruction Following: Trained on diverse datasets like OpenOrca and Airoboros 3.1, enabling it to respond to a wide range of prompts and instructions.
  • Conversational AI: Optimized for interactive chat applications, with a recommended prompt format for Username: {Input} and BotName: {Response}.
  • Code-Related Tasks: Inclusion of Magiccoder-Evol-Instruct-110k in its training suggests capabilities in understanding and generating code-related text.
  • Robustness: Training on datasets like ToxicQA and Yahoo Answers may contribute to its ability to handle varied and sometimes challenging conversational contexts.

Good For

  • General-purpose chatbots and virtual assistants requiring versatile instruction following.
  • Interactive storytelling and role-playing applications due to its conversational fine-tuning.
  • Developers experimenting with models that have been trained on a highly eclectic mix of public datasets to achieve broad utility.

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