You're looking at a specific version of this model. Jump to the model overview.

lucataco /qwen2-57b-a14b-instruct:fc67fa3f

Input

string
Shift + Return to add a new line

Prompt

Default: ""

string
Shift + Return to add a new line

System prompt to send to the model. This is prepended to the prompt and helps guide system behavior. Ignored for non-chat models.

Default: "You are a helpful assistant."

integer

The minimum number of tokens the model should generate as output.

Default: 0

integer

The maximum number of tokens the model should generate as output.

Default: 512

number

The value used to modulate the next token probabilities.

Default: 0.6

number

A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751).

Default: 0.9

integer

The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).

Default: 50

number

Presence penalty

Default: 0

number

Frequency penalty

Default: 0

string
Shift + Return to add a new line

A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' will stop generation at the first instance of 'end' or '<stop>'.

Output

A large language model (LLM) is a type of artificial intelligence model that is trained on a massive amount of text data to generate human-like text. These models are typically trained using deep learning techniques, and they are able to generate text that is coherent and contextually appropriate, making them useful for a variety of natural language processing tasks. Some common applications of large language models include language translation, text summarization, and question answering. They are also used in chatbots and virtual assistants to enable more natural and realistic conversations with users. Large language models are often referred to as "generative models" because they are able to generate new text based on the patterns they have learned from the training data.
Generated in