NeuralHermes-2.5-Mistral-7B-GGUF
Maintainer: TheBloke
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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 NeuralHermes-2.5-Mistral-7B-GGUF is a large language model created by Maxime Labonne. It is based on the original NeuralHermes 2.5 Mistral 7B model and has been quantized to a GGUF format, which is a new model file type introduced by the llama.cpp team. This allows the model to be used with a variety of clients and libraries that support the GGUF format, including llama.cpp, text-generation-webui, and LM Studio.
The CapybaraHermes-2.5-Mistral-7B-GGUF is a similar model created by Argilla, which is a preference-tuned version of the original OpenHermes-2.5-Mistral-7B model. It has been designed to perform better on multi-turn conversational tasks.
The OpenHermes-2.5-neural-chat-7B-v3-1-7B-GGUF is another related model, created by Yaz alk, which is a merge of the teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-1 models, fine-tuned for chat-style interactions.
Model inputs and outputs
The NeuralHermes-2.5-Mistral-7B-GGUF model is a generative language model that can be used for a variety of text-based tasks, such as text generation, question answering, and dialogue. It takes in natural language prompts as input and generates relevant text outputs.
Inputs
- Prompts: Natural language text prompts that the model uses to generate relevant output.
Outputs
- Generated text: The model's response to the provided prompt, which can range from a single sentence to multiple paragraphs, depending on the task and the specific input.
Capabilities
The NeuralHermes-2.5-Mistral-7B-GGUF model is capable of generating coherent and contextually relevant text across a wide range of domains, including creative writing, analytical tasks, and open-ended conversations. It has been shown to perform well on benchmarks like AGIEval, GPT4All, and TruthfulQA.
The CapybaraHermes-2.5-Mistral-7B-GGUF model in particular has demonstrated improved performance on multi-turn conversational tasks, as measured by the MTBench benchmark.
What can I use it for?
The NeuralHermes-2.5-Mistral-7B-GGUF and related models can be used for a variety of applications, such as:
- Content generation: Generating articles, stories, scripts, or other long-form text content.
- Dialogue systems: Building chatbots and virtual assistants for customer service, education, or entertainment.
- Question answering: Providing informative responses to factual questions across a wide range of topics.
- Creative writing: Assisting with ideation, plot development, and character creation for novels, scripts, and other creative works.
These models can be particularly useful for companies or individuals looking to automate or augment their content creation and customer interaction processes.
Things to try
One interesting aspect of the NeuralHermes-2.5-Mistral-7B-GGUF model is its ability to generate coherent and contextually relevant text over extended sequences. This makes it well-suited for tasks that require longer-form output, such as writing summaries, reports, or even short stories.
Another key feature is the model's performance on multi-turn conversational tasks, as demonstrated by the CapybaraHermes-2.5-Mistral-7B-GGUF model. This suggests that the model may be particularly useful for building interactive chatbots or virtual assistants that can engage in natural, back-and-forth dialogue.
Developers and researchers may want to experiment with fine-tuning these models on specialized datasets or for specific tasks to further enhance their capabilities in areas of interest.
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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