Meta-Llama-3-8B-Instruct-GGUF

Maintainer: NousResearch

Total Score

109

Last updated 5/23/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The Meta-Llama-3-8B-Instruct model is part of the Meta Llama 3 family of large language models (LLMs) developed and released by Meta. This 8 billion parameter model is a pretrained and instruction-tuned generative text model optimized for dialogue use cases. The Llama 3 models outperform many open-source chat models on common industry benchmarks while prioritizing helpfulness and safety.

Similar models in the Llama 3 family include the Meta-Llama-3-8B and Meta-Llama-3-70B variants, which come in 8 billion and 70 billion parameter sizes respectively. All Llama 3 models use an optimized transformer architecture and leverage techniques like supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences.

Model inputs and outputs

Inputs

  • Text: The Meta-Llama-3-8B-Instruct model takes text as input.

Outputs

  • Text and code: The model generates text and code outputs.

Capabilities

The Meta-Llama-3-8B-Instruct model is capable of engaging in open-ended dialogue, answering questions, and assisting with a variety of natural language tasks. Its instruction-tuning makes it well-suited for assistant-like chat applications that require helpfulness and safety. The model can also be fine-tuned for specialized use cases beyond dialogue.

What can I use it for?

The Meta-Llama-3-8B-Instruct model is intended for commercial and research use in English. Developers can leverage it to build chatbots, question-answering systems, and other language AI applications that require a helpful and safe assistant. The pretrained model can also be adapted for natural language generation tasks beyond dialogue.

Things to try

Try using the Meta-Llama-3-8B-Instruct model to engage in open-ended conversations and see how it responds. You can also experiment with providing it with specific tasks or prompts to gauge its capabilities. Remember to leverage the provided safety resources when deploying the model in production to mitigate potential risks.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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