OpenHermes-2.5-neural-chat-7B-v3-1-7B-GGUF
Maintainer: TheBloke
51
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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 OpenHermes-2.5-neural-chat-7B-v3-1-7B-GGUF model is a 7B parameter chat-oriented language model created by Yaz alk and maintained by TheBloke. It is built on the OpenHermes 2.5 Neural Chat 7B V3.1 7B model and has been quantized to use the new GGUF format. GGUF offers advantages over the previous GGML format, including better tokenization and support for special tokens.
This model is part of a larger collection of quantized GGUF models maintained by TheBloke, including similar chat-focused models like neural-chat-7B-v3-1-GGUF and openchat_3.5-GGUF. These models leverage the work of various researchers and teams, including Intel, OpenChat, and Argilla.
Model inputs and outputs
Inputs
- Text prompts: The model accepts free-form text prompts as input, which it can use to generate coherent and contextual responses.
Outputs
- Text completions: The primary output of the model is generated text, which can range from short, direct responses to more elaborated multi-sentence outputs.
Capabilities
The OpenHermes-2.5-neural-chat-7B-v3-1-7B-GGUF model is designed for open-ended conversation and dialogue. It can engage in natural back-and-forth exchanges, demonstrating an understanding of context and the ability to provide relevant and coherent responses. The model has been trained on a large corpus of online data and has been fine-tuned for chat-oriented tasks, making it well-suited for applications like virtual assistants, chatbots, and conversational interfaces.
What can I use it for?
This model could be used to power a variety of conversational AI applications, such as:
- Virtual assistants: Integrate the model into a virtual assistant system to handle natural language interactions and provide helpful responses to user queries.
- Chatbots: Deploy the model as the conversational engine behind a chatbot, enabling engaging and contextual dialogues on a wide range of topics.
- Conversational interfaces: Incorporate the model into user interfaces that require natural language interaction, such as messaging apps, customer service platforms, or educational tools.
Things to try
One interesting aspect of the OpenHermes-2.5-neural-chat-7B-v3-1-7B-GGUF model is its ability to engage in multi-turn conversations. Try providing the model with a series of related prompts and observe how it maintains context and coherence throughout the dialogue. Additionally, experiment with different types of prompts, such as open-ended questions, task-oriented instructions, or creative storytelling, to see the range of responses the model can generate.
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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