VerusCommunity/Llama-3-VerusGPT

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Jun 10, 2024License:llama3Architecture:Transformer0.0K Cold

Llama 3 VerusGPT is an 8 billion parameter, instruction-tuned Llama 3 model developed by Evan Armstrong for the Verus Community, with an 8192 token context length. It is a domain-expert LLM specifically trained to answer questions about the Verus Project, its protocol, and community. This model excels at providing detailed information on Verus-specific topics and general crypto concepts, aiming to educate users about the Verus ecosystem.

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Llama 3 VerusGPT: A Domain-Expert for the Verus Ecosystem

Llama 3 VerusGPT is an 8 billion parameter, instruction-tuned model built upon Meta's Llama 3 architecture, developed by Evan Armstrong for the Verus Community. This model is uniquely specialized as a domain-expert AI, focusing on the Verus Project, its protocol, and community. Its primary goal is to educate users about Verus and broader blockchain concepts.

Key Capabilities

  • Verus Protocol Expertise: Provides in-depth answers regarding the Verus multi-chain and multi-currency blockchain protocol.
  • Community Ethos: Understands and communicates the Verus community's mission and values.
  • General Crypto Advisor: Can assist with understanding complex blockchain concepts beyond Verus.
  • Novel Training Approach: Incorporates factual information through a unique finetuning method using a large system prompt for latent space activation, enhancing factual recall at low temperatures.
  • Open-Source Development: Trained using custom dataset generation code and datasets, which are open-sourced to demonstrate how domain-expert LLMs can be rapidly developed.

Good For

  • Learning about the Verus Project, Protocol, and community.
  • Understanding difficult blockchain concepts as a general crypto advisor.
  • Developers interested in open-source domain-expert LLM training methodologies.

Note: The model is highly sensitive to inference settings; using a very low temperature (around 0.05 or lower) and the recommended system prompt is crucial for optimal performance. It is not optimized for general assistant-style use cases and may hallucinate on advanced or rapidly changing numerical data.

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