duyntnet/nexusraven-v2-13b-imatrix-gguf overview

{
  "metadata": {},
  "card_data": {
    "license": "other",
    "language": [
      "en"
    ],
    "pipeline_tag": "text-generation",
    "inference": false,
    "tags": [
      "transformers",
      "gguf",
      "imatrix",
      "NexusRaven-V2-13B"
    ],
    "frontmatter": {
      "license": "other",
      "language": [
        "en"
      ],
      "pipeline_tag": "text-generation",
      "inference": "false",
      "tags": [
        "transformers",
        "gguf",
        "imatrix",
        "NexusRaven-V2-13B"
      ]
    },
    "hero_image_url": "",
    "summary": "### Quickstart You can run the model on a GPU using the following code. ``python # Please pip install transformers accelerate from transformers import pipeline pipeline = pipeline( \"text-generation\", model=\"Nexusflow/NexusRaven-V2-13B\", torch_dtype=\"auto\", device_map=\"auto\", ) prompt_template = \\ ''' Function: def get_weather_data(coordinates): \"\"\" Fetches weather data from the Open-Meteo API for the given latitude and longitude. Args: coordinates (tuple): The latitude of the location. Returns: float: The current temperature in the coordinates you've asked for \"\"\" Function: def get_coordinates_from_city(city_name): \"\"\" Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API. Args: city_name (str): The name of the city. Returns: tuple: The latitude and longitude of the city. \"\"\" User Query: {query} ''' prompt = prompt_template.format(query=\"What's the weather like in Seattle right now?\") result = pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0][\"generated_text\"] print (result) ` This should generate the following: ` Call: get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle')) Thought: The function call get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle')) answers the question \"What's the weather like in Seattle right now?\" by following these steps: 1. get_coordinates_from_city(city_name='Seattle'): This function call fetches the latitude and longitude of the city \"Seattle\" using the Maps.co Geocoding API. 2. get_weather_data(coordinates=...): This function call fetches the current weather data for the coordinates returned by the previous function call. Therefore, the function call get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle')) answers the question \"What's the weather like in Seattle right now?\" by first fetching the coordinates of the city \"Seattle\" and then fetching the current weather data for those coordinates. `` If you would like to prevent the generation of the explanation of the function call (for example, to save on inference tokens), please set a stopping criteria of \\. Please follow this prompting template to maximize the performance of RavenV2. ### Using with OpenAI FC Schematics If you currently have a workflow that is built around OpenAI's function calling and you want to try NexusRaven-V2, we have a package that helps you drop in NexusRaven-V2. ### Using With LangChain We've also included a small demo for using Raven with langchain!",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: other\nlanguage:\n- en\npipeline_tag: text-generation\ninference: false\ntags:\n- transformers\n- gguf\n- imatrix\n- NexusRaven-V2-13B\n---\nQuantizations of https://huggingface.co/Nexusflow/NexusRaven-V2-13B\n\n# From original readme\n\n### Quickstart\nYou can run the model on a GPU using the following code. \n```python\n# Please `pip install transformers accelerate`\nfrom transformers import pipeline\n\n\npipeline = pipeline(\n    \"text-generation\",\n    model=\"Nexusflow/NexusRaven-V2-13B\",\n    torch_dtype=\"auto\",\n    device_map=\"auto\",\n)\n\nprompt_template = \\\n'''\nFunction:\ndef get_weather_data(coordinates):\n    \"\"\"\n    Fetches weather data from the Open-Meteo API for the given latitude and longitude.\n\n    Args:\n    coordinates (tuple): The latitude of the location.\n\n    Returns:\n    float: The current temperature in the coordinates you've asked for\n    \"\"\"\n\nFunction:\ndef get_coordinates_from_city(city_name):\n    \"\"\"\n    Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.\n\n    Args:\n    city_name (str): The name of the city.\n\n    Returns:\n    tuple: The latitude and longitude of the city.\n    \"\"\"\n\nUser Query: {query}<human_end>\n\n'''\n\nprompt = prompt_template.format(query=\"What's the weather like in Seattle right now?\")\n\nresult = pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0][\"generated_text\"]\nprint (result)\n```\n\nThis should generate the following:\n```\nCall: get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))<bot_end>\nThought: The function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question \"What's the weather like in Seattle right now?\" by following these steps:\n\n1. `get_coordinates_from_city(city_name='Seattle')`: This function call fetches the latitude and longitude of the city \"Seattle\" using the Maps.co Geocoding API.\n2. `get_weather_data(coordinates=...)`: This function call fetches the current weather data for the coordinates returned by the previous function call.\n\nTherefore, the function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question \"What's the weather like in Seattle right now?\" by first fetching the coordinates of the city \"Seattle\" and then fetching the current weather data for those coordinates.\n```\n\nIf you would like to prevent the generation of the explanation of the function call (for example, to save on inference tokens), please set a stopping criteria of \\<bot_end\\>. \n\nPlease follow this prompting template to maximize the performance of RavenV2. \n\n### Using with OpenAI FC Schematics\n\n[If you currently have a workflow that is built around OpenAI's function calling and you want to try NexusRaven-V2, we have a package that helps you drop in NexusRaven-V2.](https://github.com/nexusflowai/nexusraven-pip)\n\n### Using With LangChain\n\nWe've also included a [small demo for using Raven with langchain](langdemo.py)! ",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "imatrix",
    "NexusRaven-V2-13B",
    "text-generation",
    "en",
    "license:other",
    "region:us"
  ],
  "likes": 2,
  "downloads": 172,
  "gated": false,
  "private": false,
  "last_modified": "2024-05-09T09:17:24.000Z",
  "created_at": "2024-05-09T04:48:16.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
}

{
  "_id": "663c55907d5f1fbe088db69e",
  "id": "duyntnet/NexusRaven-V2-13B-imatrix-GGUF",
  "modelId": "duyntnet/NexusRaven-V2-13B-imatrix-GGUF",
  "sha": "63f87df681eab13f3fc0446a38a70574529f3068",
  "createdAt": "2024-05-09T04:48:16.000Z",
  "lastModified": "2024-05-09T09:17:24.000Z",
  "author": "duyntnet",
  "downloads": 172,
  "likes": 2,
  "gated": false,
  "private": false,
  "pipeline_tag": "text-generation",
  "library_name": "transformers",
  "siblings_count": 29
}