llama-3-8b-bnb-4bit

Maintainer: unsloth

Total Score

112

Last updated 5/28/2024

🛠️

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 llama-3-8b-bnb-4bit model is a version of the Meta Llama 3 language model that has been quantized to 4-bit precision using the bitsandbytes library. This model was created by the maintainer unsloth and is designed to provide faster finetuning and lower memory usage compared to the original Llama 3 model.

The maintainer has also created quantized 4-bit versions of other large language models like Gemma 7b, Mistral 7b, Llama-2 7b, and TinyLlama, all of which can be finetuned 2-5x faster with 43-74% less memory usage.

Model inputs and outputs

Inputs

  • Natural language text prompts

Outputs

  • Natural language text continuations and completions

Capabilities

The llama-3-8b-bnb-4bit model can be used for a variety of text generation tasks, such as language modeling, text summarization, and question answering. The maintainer has provided examples of using this model to finetune on custom datasets and export the resulting models for use in other applications.

What can I use it for?

The llama-3-8b-bnb-4bit model can be a useful starting point for a wide range of natural language processing projects that require a large language model with reduced memory and faster finetuning times. For example, you could use this model to build chatbots, content generation tools, or other applications that rely on text-based AI. The maintainer has also provided a Colab notebook to help get you started with finetuning the model.

Things to try

One interesting aspect of the llama-3-8b-bnb-4bit model is its ability to be finetuned quickly and efficiently. This could make it a good choice for quickly iterating on new ideas or testing different approaches to a problem. Additionally, the reduced memory usage of the 4-bit quantized model could allow you to run it on less powerful hardware, opening up more opportunities to experiment and deploy your models.



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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