llama-2-70b-Guanaco-QLoRA-fp16

Maintainer: TheBloke

Total Score

56

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-2-70b-Guanaco-QLoRA-fp16 model is a 70 billion parameter large language model created by Mikael110. It is a quantized and compressed version of the original Llama 2 70B model, finetuned using QLoRA (Quantization and Low-Rank Adaptation) to improve performance and reduce resource usage. The model is available in PyTorch format with 16-bit floating point precision, allowing for efficient GPU inference.

This model is part of a family of Llama 2 models created by various contributors, with different quantization levels and model sizes. TheBloke has also provided GPTQ and GGML versions of this model for GPU and CPU/GPU inference respectively.

Model inputs and outputs

Inputs

  • Text: The model accepts text input, which can be in the form of a single prompt or a conversation-style exchange.

Outputs

  • Text: The model generates text as output, continuing the input prompt or providing a response in a conversational exchange.

Capabilities

The llama-2-70b-Guanaco-QLoRA-fp16 model is a large, powerful language model capable of a wide range of natural language tasks. It has been shown to perform well on benchmarks for commonsense reasoning, world knowledge, reading comprehension, and mathematics. The model can be used for tasks such as question answering, summarization, translation, and open-ended text generation.

What can I use it for?

This model can be used for a variety of natural language processing applications, such as building chatbots, virtual assistants, or content generation tools. The lightweight, quantized nature of the model makes it suitable for deployment on resource-constrained devices or in environments with limited compute power, while still maintaining strong performance.

Things to try

One interesting aspect of this model is the use of QLoRA, which allows for efficient finetuning and adaptation of the model to specific domains or use cases. Developers could explore finetuning the model on their own datasets or for specialized tasks, leveraging the powerful base capabilities of the Llama 2 architecture.



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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