C-Nocturnum/Meta-Llama-3-8B-Instruct-cyber-abliterated

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Mar 23, 2025Architecture:Transformer Featherless Exclusive Cold

C-Nocturnum/Meta-Llama-3-8B-Instruct-cyber-abliterated is an 8 billion parameter instruction-tuned language model based on the Meta-Llama-3 architecture. This model is designed for general-purpose conversational AI tasks, leveraging its instruction-following capabilities. With an 8192-token context window, it aims to provide coherent and relevant responses across various prompts. Its primary strength lies in its ability to process and generate human-like text based on given instructions.

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Model Overview

This model, C-Nocturnum/Meta-Llama-3-8B-Instruct-cyber-abliterated, is an 8 billion parameter instruction-tuned variant of the Meta-Llama-3 architecture. It is designed to understand and follow instructions, making it suitable for a wide range of natural language processing tasks. The model features an 8192-token context window, allowing it to handle longer inputs and generate more extensive outputs while maintaining conversational coherence.

Key Capabilities

  • Instruction Following: Excels at interpreting and executing user instructions for text generation.
  • General-Purpose Text Generation: Capable of producing human-like text for various prompts and scenarios.
  • Conversational AI: Suitable for chatbot applications and interactive dialogue systems.
  • Extended Context: Benefits from an 8192-token context length for processing detailed information.

Use Cases

This model is a strong candidate for applications requiring robust instruction-following and text generation. It can be utilized for:

  • Chatbots and Virtual Assistants: Engaging in natural conversations and providing informative responses.
  • Content Creation: Generating articles, summaries, creative writing, and other textual content based on prompts.
  • Question Answering: Extracting and synthesizing information to answer specific queries.
  • Code Generation (Limited): While not specialized, its general instruction-following may assist with basic code snippets or explanations.