Llama-2-13B-GGML

Maintainer: TheBloke

Total Score

172

Last updated 5/27/2024

📉

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-GGML is a 13 billion parameter language model created by Meta. It is a larger version of the Llama 2 model, which was originally released in a 7 billion parameter size. The Llama-2-13B-GGML model has been released in a GGML format, which allows for efficient CPU and GPU inference using tools like llama.cpp and associated UIs and libraries.

Similar models include the WizardLM-7B-uncensored-GPTQ, a 7 billion parameter model created by Eric Hartford and optimized for GPU inference, as well as the Llama-2-7B and Llama-2-70B models from Meta, which are 7 billion and 70 billion parameter versions respectively.

Model inputs and outputs

The Llama-2-13B-GGML model is a text-to-text generative language model. It takes natural language text as input and generates fluent, coherent text as output.

Inputs

  • Natural language text prompts

Outputs

  • Generated natural language text
  • Completions and continuations of the input prompts

Capabilities

The Llama-2-13B-GGML model is capable of tasks like open-ended conversation, question answering, summarization, and creative text generation. With its large 13 billion parameter size, it can engage in detailed, nuanced dialogue and produce high-quality, contextual outputs.

What can I use it for?

The Llama-2-13B-GGML model can be used for a variety of natural language processing applications, such as chatbots, virtual assistants, content generation, and language understanding. Its efficient GGML format makes it well-suited for deployment on CPUs and GPUs, allowing it to be used in a wide range of real-world scenarios.

Things to try

Some interesting things to try with the Llama-2-13B-GGML model include using it for creative writing tasks, where its strong language modeling capabilities can produce evocative and imaginative text. You could also experiment with fine-tuning the model on domain-specific data to adapt it for specialized applications. Additionally, exploring the model's reasoning and commonsense understanding by posing it with complex prompts or multi-step tasks could yield valuable insights.



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-GGML

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Total Score

214

The Llama-2-7B-GGML is a variant of Meta's Llama 2 language model, created by the maintainer TheBloke. This 7 billion parameter model has been optimized for CPU and GPU inference using the GGML format. It is part of a collection of Llama 2 models ranging from 7 billion to 70 billion parameters, with both pretrained and fine-tuned versions available. The fine-tuned models, like this one, are optimized for dialogue use cases. Similar models include the Llama-2-13B-GGML and Llama-2-7B-Chat-GGML, which offer different parameter sizes and optimizations. Model inputs and outputs Inputs Text**: The Llama-2-7B-GGML model takes text as input. Outputs Text**: The model generates text as output. Capabilities The Llama-2-7B-GGML model is capable of a wide range of natural language generation tasks, including dialogue, summarization, and content creation. It has been shown to outperform many open-source chat models on benchmarks, and can provide helpful and safe responses on par with some popular closed-source models. What can I use it for? You can use the Llama-2-7B-GGML model for a variety of commercial and research applications, such as building AI assistants, content generation tools, and language understanding systems. The fine-tuned chat version is particularly well-suited for conversational AI use cases. Things to try Try prompting the Llama-2-7B-GGML model with open-ended questions or instructions to see its versatility in generating coherent and contextual responses. You can also experiment with different temperature and sampling settings to influence the creativity and diversity of the output.

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Llama-2-13B-chat-GGML

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The Llama-2-13B-chat-GGML model is a 13-billion parameter large language model created by Meta and optimized for dialogue use cases. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters and are designed for a variety of natural language generation tasks. This specific model has been converted to the GGML format, which is designed for CPU and GPU inference using tools like llama.cpp and associated libraries and UIs. The GGML format has since been superseded by GGUF, so users are encouraged to use the GGUF versions of these models going forward. Similar models include the Llama-2-7B-Chat-GGML and the Llama-2-13B-GGML, which offer smaller and larger versions of the Llama 2 architecture in the GGML format. Model Inputs and Outputs Inputs Raw text Outputs Generated text continuations Capabilities The Llama-2-13B-chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and generating coherent and context-appropriate text continuations. It has been fine-tuned to perform well on benchmarks for helpfulness and safety, making it suitable for use in assistant-like applications. What Can I Use It For? The Llama-2-13B-chat-GGML model could be used to power conversational AI assistants, chatbots, or other applications that require natural language generation and understanding. Given its strong performance on safety metrics, it may be particularly well-suited for use cases where providing helpful and trustworthy responses is important. Things to Try One interesting aspect of the Llama-2-13B-chat-GGML model is its ability to handle context and engage in multi-turn conversations. Users could try prompting the model with a series of related questions or instructions to see how it maintains coherence and builds upon previous responses. Additionally, the model's quantization options allow for tuning the balance between performance and accuracy, so users could experiment with different quantization levels to find the optimal tradeoff for their specific use case.

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Llama-2-70B-GGML

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