openhermes-2.5-mistral-7b-awq
AI model optimized for open-domain question answering.
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)
@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.
2. Pre-training: The model has been pre-trained on an extensive text dataset, which enables it to understand and generate natural language text efficiently.
3. 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.
4. Adaptation: The model can be customized (fine-tuned) to specific tasks or subject areas to achieve better performance.
5. 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
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 }