openhermes-2.5-mistral-7b

Maintainer: antoinelyset

Total Score

11

Last updated 5/17/2024

🏅

PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

openhermes-2.5-mistral-7b is a large language model based on the Mistral-7B model, developed by the creator antoinelyset. It is a 7 billion parameter model that has been further fine-tuned and optimized for various tasks. This model builds on the capabilities of similar Mistral-7B models, such as mistral-7b-v0.1, mistral-7b-instruct-v0.1, and mistral-7b-instruct-v0.2, which have shown strong performance on a variety of language tasks.

Model inputs and outputs

The openhermes-2.5-mistral-7b model takes a JSON-formatted prompt as input, which can contain an array of messages with role and content information. The model can then generate new text based on the provided prompt. The output is an array of strings, where each string represents a generated response.

Inputs

  • prompt: The JSON-formatted prompt containing an array of messages to predict on.
  • temperature: Adjusts the randomness of the outputs, with higher values resulting in more diverse and unpredictable text.
  • top_k: Specifies the number of most likely tokens to consider during the decoding process, allowing for more or less diversity in the output.
  • top_p: Specifies the percentage of most likely tokens to consider during the decoding process, also affecting the diversity of the output.
  • max_new_tokens: Determines the maximum number of new tokens the model will generate.

Outputs

  • An array of generated text, with each element representing a single response.

Capabilities

The openhermes-2.5-mistral-7b model is capable of engaging in open-ended conversations, generating coherent and contextually appropriate responses. It can be used for a variety of language-based tasks, such as text summarization, question answering, and content generation.

What can I use it for?

The openhermes-2.5-mistral-7b model can be used for a wide range of applications that involve natural language processing and generation. Some potential use cases include:

  • Conversational AI: The model can be integrated into chatbots, virtual assistants, and other conversational interfaces to provide human-like responses.
  • Content Generation: The model can be used to generate various types of text, such as articles, stories, or product descriptions.
  • Summarization: The model can be used to summarize longer pieces of text, distilling the key information and insights.
  • Question Answering: The model can be used to answer questions on a wide range of topics, drawing from its broad knowledge base.

Things to try

One interesting aspect of the openhermes-2.5-mistral-7b model is its ability to generate diverse and creative responses. By adjusting the temperature and top-k/top-p parameters, you can experiment with the level of randomness and variety in the output. This can be particularly useful for tasks like story generation or open-ended brainstorming, where you want to explore a range of possible ideas and directions.

Additionally, you can try fine-tuning the model on domain-specific data to further specialize its capabilities for your particular use case. This can involve updating the model's parameters or incorporating additional training data to enhance its performance on specific tasks or topics.



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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