sheep-duck-llama-2-70b-v1-1-gguf

Maintainer: andreasjansson

Total Score

218

Last updated 5/23/2024

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PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The sheep-duck-llama-2-70b-v1-1-gguf is a large language model developed by andreasjansson. This model is part of a family of related models, including blip-2, llava-v1.6-vicuna-7b, llava-v1.6-vicuna-13b, llama-2-7b, and nous-hermes-llama2-awq.

Model inputs and outputs

The sheep-duck-llama-2-70b-v1-1-gguf model takes a variety of inputs, including prompts, grammar specifications, JSON schemas, and various parameters to control the model's behavior. The outputs are an array of strings, which can be concatenated to form the model's response.

Inputs

  • Prompt: The input text that the model will use to generate a response.
  • Grammar: A grammar specification in GBNF format that constrains the output.
  • Jsonschema: A JSON schema that defines the structure of the desired output.
  • Max Tokens: The maximum number of tokens to include in the generated output.
  • Temperature: A parameter that controls the randomness of the generated output.
  • Mirostat Mode: The sampling mode to use, which can be disabled or set to one of several modes.
  • Repeat Penalty: A penalty applied to repeated tokens in the output.
  • Mirostat Entropy: The target entropy for the Mirostat sampling mode.
  • Presence Penalty: A penalty applied to tokens that have appeared in the output before.
  • Frequency Penalty: A penalty applied to tokens that have appeared frequently in the output.
  • Mirostat Learning Rate: The learning rate for the Mirostat sampling mode.

Outputs

  • An array of strings that represents the model's generated response.

Capabilities

The sheep-duck-llama-2-70b-v1-1-gguf model is a powerful language model that can be used for a variety of tasks, such as text generation, question answering, and language understanding. It can generate coherent and relevant text based on the provided input, and its capabilities can be further customized through the use of input parameters.

What can I use it for?

The sheep-duck-llama-2-70b-v1-1-gguf model can be used for a wide range of applications, such as customer service chatbots, content generation, and creative writing. By leveraging the model's language understanding and generation capabilities, users can automate and scale tasks that involve natural language processing. Additionally, the model's flexibility allows it to be integrated into various business and research workflows.

Things to try

One interesting aspect of the sheep-duck-llama-2-70b-v1-1-gguf model is its ability to generate text that adheres to specific constraints, such as a predefined grammar or JSON schema. This can be particularly useful for generating structured data or content that needs to follow a particular format. Additionally, experimenting with the various input parameters, such as temperature and repeat penalty, can lead to different styles and qualities of generated text, allowing users to find the optimal configuration for their specific use case.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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