dolphin-2.0-mistral-7B-GGUF
Maintainer: TheBloke
48
⚙️
| 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-2.0-mistral-7B-GGUF is a large language model created by Eric Hartford and maintained by TheBloke. It is based on the original Dolphin 2.0 Mistral 7B model, which was trained on a dataset curated by Hartford. This model is available in GGUF format, a new model format introduced by the llama.cpp team that replaces the older GGML format.
Similar models in the Dolphin series include the dolphin-2.2.1-mistral-7B-GGUF and dolphin-2.1-mistral-7B-GGUF, which offer incremental improvements and updates over the original Dolphin 2.0 model.
Model inputs and outputs
The dolphin-2.0-mistral-7B-GGUF model takes natural language inputs and generates coherent text outputs. It uses the ChatML prompt format, which includes system and user message segments.
Inputs
- Prompts: Natural language prompts or messages from the user
Outputs
- Text generation: The model generates relevant and coherent text in response to the input prompts
Capabilities
The dolphin-2.0-mistral-7B-GGUF model is capable of a wide range of text-to-text tasks, such as language translation, question answering, summarization, and open-ended conversation. It has been trained on a large and diverse dataset, giving it broad knowledge and capabilities.
One notable capability of this model is its ability to engage in multi-turn conversations. It can understand and respond to context, allowing for more natural and coherent dialogue.
What can I use it for?
The dolphin-2.0-mistral-7B-GGUF model can be used for a variety of applications that require natural language processing, such as:
- Chatbots and virtual assistants: The model's conversation capabilities make it well-suited for building chatbots and virtual assistants that can engage in natural dialogue.
- Content generation: The model can be used to generate text for a wide range of applications, such as articles, stories, or creative writing.
- Question answering: The model can be used to build systems that can answer questions and provide information to users.
- Language translation: While not specifically designed for translation, the model's language understanding capabilities could be leveraged for translation tasks.
Things to try
One interesting aspect of the dolphin-2.0-mistral-7B-GGUF model is its uncensored nature. The model has been trained on a dataset that has been filtered to remove alignment and bias, making it more compliant but also potentially less constrained in its outputs. This could be useful for certain applications, but users should be aware of the potential risks and take appropriate measures to ensure the model is used responsibly.
Another thing to try with this model is exploring its multi-turn conversation capabilities. By engaging the model in a series of back-and-forth messages, you can see how it maintains context and provides coherent responses over the course of a longer dialogue.
Overall, the dolphin-2.0-mistral-7B-GGUF model appears to be a powerful and versatile language model with a wide range of potential applications. Its GGUF format and support for a variety of client libraries and tools make it accessible and easy to integrate into various projects.
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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