dolphin-2_2-yi-34b-GGUF
Maintainer: TheBloke
46
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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 dolphin-2_2-yi-34b-GGUF model is a large language model created by Eric Hartford and supported by a grant from andreessen horowitz (a16z). It is based on the Dolphin 2.2 Yi 34B model, which was trained on the Dolphin dataset, an open-source implementation of Microsoft's Orca. The model has been quantized using GGUF, a new format introduced by the llama.cpp team, which provides a more efficient way to store and run the model.
The dolphin-2_2-yi-34b-GGUF model is part of a series of Dolphin models released by TheBloke, a prominent AI researcher and model creator. Similar models in this series include the dolphin-2.1-mistral-7B-GGUF, dolphin-2.0-mistral-7B-GGUF, and dolphin-2_6-phi-2-GGUF.
Model inputs and outputs
Inputs
- The model expects input in the ChatML format, with separate sections for the system message, user prompt, and assistant response.
Outputs
- The model generates text continuations in response to the provided prompt, in the ChatML format.
- The model can generate long-form text, with the ability to handle extended sequences up to 32,768 tokens.
Capabilities
The dolphin-2_2-yi-34b-GGUF model is a powerful language model capable of a wide range of tasks, including:
- Natural Language Generation: The model can generate coherent and contextually relevant text continuations based on the provided prompt.
- Question Answering: The model can provide informative answers to a variety of questions across different domains.
- Summarization: The model can summarize longer passages of text into concise and meaningful summaries.
- Dialogue and Conversation: The model can engage in multi-turn conversations, demonstrating an understanding of context and the ability to provide relevant and empathetic responses.
What can I use it for?
The dolphin-2_2-yi-34b-GGUF model can be used for a variety of applications, such as:
- Content Creation: The model can assist with generating articles, stories, scripts, and other forms of written content.
- Chatbots and Virtual Assistants: The model can be used to power conversational AI systems, providing natural and engaging responses to user queries.
- Task Automation: The model can be fine-tuned or used as a component in larger systems to automate various text-based tasks, such as customer service inquiries, report writing, or data analysis.
- Educational and Research Purposes: The model can be used for educational purposes, such as language learning or as a tool for AI and machine learning research.
Things to try
One interesting aspect of the dolphin-2_2-yi-34b-GGUF model is its ability to handle extended sequences of text. This can be useful for tasks like long-form content generation, where the model can maintain coherence and context over longer passages. You can experiment with prompting the model to generate multi-paragraph essays, stories, or other long-form text to see how it performs.
Another intriguing capability of the model is its potential for engaging in more natural, multi-turn conversations. You can try interacting with the model in a back-and-forth dialogue, providing context and follow-up questions, to see how it responds and how it maintains the flow of the conversation.
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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