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04.6.13.09 llama-2-7b-chat-int8

Model Description​

The @cf/meta/llama-2-7b-chat-int8 model includes two nodes:

  • llama-2-7b-chat-int8 Prompt (preview)
  • llama-2-7b-chat-int8 With History (preview)
note

Model ID: @cf/meta/llama-2-7b-chat-int8. Quantized (int8) generative text model with 7 billion parameters from Meta.

caution

Maximum number of input tokens: 2048. Output sizes: 1800

The model is designed for natural language communication tasks such as chatbots, assistants, answer generation, etc. The main features and applications of this model are:

  1. Model size: The LLaMA-2-7B model has a size of 2.7 billion parameters, which allows it to achieve high performance and quality of generated text.
  1. Use of int8 quantization: The model uses int8 quantization, which reduces the model size and speeds up the output without significant loss in quality.
  1. Optimization for chat tasks: The model has been specially trained and optimized for conversational communication tasks such as user support, answering questions, and generating chat responses.
  1. Openness and Accessibility: The model is open and accessible for use by researchers and developers. This makes it easy to integrate it into various applications and systems.

This model can be used in the following scenarios:

  • Building dialog systems and chatbots
  • Generating answers to user questions
  • Automating customer support
  • Creating assistants with the ability to communicate in natural language
  • Extending the capabilities of existing applications by adding communication features

Example of launching a node​

A description of the node fields can be found here.

Let's run the llama-2-7b-chat-int8 Prompt (preview) node to process the text and generate a response with parameters:

  • User Prompt - How do you handle complex tasks such as natural language processing or image recognition?
  • 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
{
  "result": {
    "errors": [],
    "messages": [],
    "result": {
      "response": "Hello there! *smiles* I'm glad you're interested in how I handle complex tasks. *adjusts glasses*\nWell, you see, natural language processing (NLP) and image recognition are just two of the many advanced technologies that I've been trained on. *nods* My training data includes a vast amount of text, images, and other forms of data that I've learned to process and analyze.\nWhen it comes to NLP, I use a combination of machine learning algorithms and statistical models to analyze and understand the nuances of language. *leans in* I can recognize patterns in language, identify sentiment, and even generate text that's contextually appropriate. *winks*\nAs for image recognition, I use a similar approach. I'm trained on a vast dataset of images, each labeled with the object or scene they depict. *points* By analyzing these images, I've learned to identify patterns and features that are unique to each object or scene. *nods*\nBut it's not just about recognizing images or understanding language. *smiles* I'm also able to learn and improve over time, thanks to the power of machine"
    },
    "success": true
  }
}