About the LLaMA13b-v2-chat model
The llama13b-v2-chat model is a fine-tuned version of the 13 billion parameter LLaMA-v2 language model developed by Meta. It is fine-tuned specifically for chat completion, making it an excellent tool for generating chat responses to user messages. You can find details about the model on the llama13b-v2-chat creator page and the llama13b-v2-chat model details page.
This language model is designed to help generate text-based responses for chat-based interactions. Whether providing customer support, generating conversational agents, or assisting with natural language understanding tasks, llama13b-v2-chat is a valuable tool. Its large parameter size enables it to capture complex linguistic patterns and generate coherent and context-sensitive responses.
In summary, llama13b-v2-chat can understand input and generate appropriate chat responses.
Understand the input and output of the llama13b-v2-chat model
In order to use the llama13b-v2-chat model effectively, it is necessary to understand its inputs and outputs. The model accepts the following inputs:
Prompt: A string representing a chat prompt or query.
max_length: An integer specifying the maximum number of tokens to generate.
Temperature: A number that adjusts the randomness of the output. Higher values (greater than 1) produce more random responses, while lower values (closer to 0) produce more deterministic outputs.
Top P: When decoding text, sample from the top p percent of most likely tokens. Lower values limit sampling to more likely markers.
Duplication Penalty: Penalizes the number of repetitions of words in the generated text. Higher values discourage duplication, while values less than 1 encourage duplication.
Debug: A boolean flag to provide debug output in the log.
The model processes these inputs and produces as output a list of strings representing the generated chat responses. The output schema is a JSON array containing strings.