Nous-Hermes-Llama2-GGML
Maintainer: TheBloke
100
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The Nous-Hermes-Llama2-GGML model is a version of the Nous Hermes Llama 2 13B language model that has been converted to the GGML format. It was created by NousResearch and is maintained by TheBloke. Similar models include the Llama-2-13B-GGML and Llama-2-13B-chat-GGML models, also maintained by TheBloke.
Model inputs and outputs
The Nous-Hermes-Llama2-GGML model is a text-to-text transformer model that takes in text as input and generates text as output. It can be used for a variety of natural language processing tasks such as language generation, text summarization, and question answering.
Inputs
- Text: The model takes in text as input, which can be in the form of a sentence, paragraph, or longer document.
Outputs
- Text: The model generates text as output, which can be in the form of a continuation of the input text, a summarization, or a response to a query.
Capabilities
The Nous-Hermes-Llama2-GGML model is capable of generating human-like text on a wide range of topics. It can be used for tasks such as writing articles, stories, or dialogue, answering questions, and summarizing information. The model has been trained on a large corpus of text data and can draw upon a broad knowledge base to generate coherent and contextually relevant output.
What can I use it for?
The Nous-Hermes-Llama2-GGML model can be used for a variety of natural language processing applications, such as content creation, customer service chatbots, language learning tools, and research and development. The GGML format makes the model compatible with a range of software tools and libraries, including text-generation-webui, KoboldCpp, and LM Studio, which can be used to incorporate the model into custom applications.
Things to try
One interesting aspect of the Nous-Hermes-Llama2-GGML model is its ability to generate text in a variety of styles and tones. Depending on the prompt or instructions provided, the model can produce output that ranges from formal and informative to creative and imaginative. Experimenting with different prompts and parameters can reveal the model's versatility and uncover new applications.
Additionally, the model's GGML format allows for efficient CPU and GPU-accelerated inference, making it a practical choice for real-time text generation applications. Exploring the performance characteristics of the model across different hardware configurations can help identify the optimal deployment scenarios.
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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