Llama-2-13B-GPTQ

Maintainer: TheBloke

Total Score

118

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-2-13B-GPTQ model is a quantized version of Meta's 13B-parameter Llama 2 large language model. It was created by TheBloke, who has made several optimized GPTQ and GGUF versions of the Llama 2 models available on Hugging Face. This model provides a balance between performance, size, and resource usage compared to other similar quantized Llama 2 models like the Llama-2-7B-GPTQ and Llama-2-70B-GPTQ.

Model inputs and outputs

Inputs

  • Text: The model takes text prompts as input, which it then uses to generate additional text.

Outputs

  • Text: The model outputs generated text, which can be used for a variety of natural language tasks such as dialogue, summarization, and content creation.

Capabilities

The Llama-2-13B-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and generating human-like text on a wide range of topics. It performs well on commonsense reasoning, world knowledge, and reading comprehension tasks. The model has also been fine-tuned for safety and helpfulness, making it suitable for use in assistant-like applications.

What can I use it for?

You can use the Llama-2-13B-GPTQ model for a variety of natural language processing tasks, such as:

  • Chatbots and virtual assistants: The model's dialogue capabilities make it well-suited for building conversational AI assistants.
  • Content generation: You can use the model to generate text for things like articles, stories, and social media posts.
  • Question answering: The model can be used to build systems that can answer questions on a wide range of subjects.
  • Summarization: The model can be used to summarize long passages of text.

Things to try

One interesting thing to try with the Llama-2-13B-GPTQ model is to experiment with different temperature and top-k/top-p sampling settings to see how they affect the model's output. Higher temperatures can lead to more diverse and creative text, while lower temperatures result in more coherent and focused output. Adjusting these settings can help you find the right balance for your specific use case.

Another interesting experiment is to use the model in a few-shot or zero-shot learning setting, where you provide the model with just a few examples or no examples at all of the task you want it to perform. This can help you understand the model's few-shot and zero-shot capabilities, and how it can be adapted to new tasks with minimal additional training.



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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Llama-2-7B-GPTQ

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Llama-2-7B-Chat-GPTQ

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Llama-2-70B-GPTQ

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Total Score

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