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04.6.13.15 openhermes-2.5-mistral-7b-awq

Model Description​

The @hf/thebloke/openhermes-2.5-mistral-7b-awq model includes two nodes:

  • openhermes-2.5-mistral-7b-awq Prompt (preview)
  • openhermes-2.5-mistral-7b-awq With History (preview)
note

Model ID: @hf/thebloke/openhermes-2.5-mistral-7b-awq. OpenHermes 2.5 Mistral 7B is a modern Mistral Fine-tune, a continuation of the OpenHermes 2 model that was trained on additional code datasets.

The OpenHermes-2.5-Mistral-7B-AWQ model is used for a variety of natural language processing tasks. Some of the main characteristics and uses of this model are:

  1. Scale: The model contains about 7 billion parameters, which makes it large and powerful enough for natural language tasks.
  1. Pre-training: The model has been pre-trained on an extensive text dataset, which enables it to understand and generate natural language text efficiently.
  1. Application: The model can be used for a wide range of natural language processing tasks such as text generation, question answering, paraphrasing, topic modeling, etc.
  1. Adaptation: The model can be customized (fine-tuned) to specific tasks or subject areas to achieve better performance.
  1. Openness: As the name implies, the OpenHermes-2.5-Mistral-7B-AWQ model is part of an open platform, which allows researchers and developers to explore it and use it in their projects.

Thus, this model is a powerful tool for a wide range of tasks related to natural language processing and can be customized for the specific needs of different applications and research.

Example of launching a node​

A description of the node fields can be found here.

Let's run the openhermes-2.5-mistral-7b-awq Prompt (preview) node to process the text and generate a response with parameters:

  • User Prompt - Generate code to determine the number of days until the new year;
  • Max Tokens (Answer Size) - 256.

The output of the node execution is JSON:

  • with a response to the "response" request;
  • with the status of the action "success": true.
JSON
{
  "errors": [],
  "messages": [],
  "result": {
    "response": "To determine the number of days until the new year, you can use the following Python code:\n\n\`\`\`python\nimport datetime\n\ndef days_until_new_year():\n    today = datetime.datetime.now()\n    new_year = datetime.datetime(today.year + 1, 1, 1)\n    return (new_year - today).days\n\nprint(days_until_new_year())\n\`\`\`\n\nThis code first imports the \`datetime\` module, which provides classes for manipulating dates and times. The \`days_until_new_year()\` function then gets the current date and time using \`datetime.datetime.now()\`. It creates a \`datetime\` object for the first day of the next year using \`datetime.datetime(today.year + 1, 1, 1)\`. Finally, it calculates the difference between the two dates and returns the number of days. The \`print()\` statement calls the function and displays the result."
  },
  "success": true
}