llama-2-70b-chat-gguf

Maintainer: andreasjansson

Total Score

1

Last updated 6/7/2024
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Model overview

llama-2-70b-chat-gguf is a large language model developed by Replicate that builds on the Llama 2 architecture. It has support for grammar-based decoding, allowing for more structured and controlled text generation. This model is part of a family of Llama 2 models created by Replicate, including the llama-2-13b-chat-gguf, codellama-7b-instruct-gguf, llama-2-7b-embeddings, llama-2-7b-chat, and llama-2-7b models.

Model inputs and outputs

llama-2-70b-chat-gguf takes a prompt as input, along with optional parameters such as top-k, top-p, temperature, and repetition penalty. The model can also accept a grammar in GBNF format or a JSON schema to guide the generation process. The output is an array of text strings, representing the generated response.

Inputs

  • Prompt: The input text to be completed or continued.
  • Grammar: A grammar in GBNF format to constrain the generated output.
  • Jsonschema: A JSON schema that defines the structure of the desired output.
  • Max Tokens: The maximum number of tokens to generate.
  • Temperature: Controls the randomness of the generated text.
  • Mirostat Mode: Selects the sampling mode, including Disabled, Mirostat1, and Mirostat2.
  • Repeat Penalty: Applies a penalty to repeated tokens.
  • Mirostat Entropy: The target entropy for the Mirostat sampling mode.
  • Presence Penalty: Applies a penalty to tokens that have already appeared in the output.
  • Frequency Penalty: Applies a penalty to tokens that have appeared frequently in the output.
  • Mirostat Learning Rate: The learning rate for the Mirostat sampling mode.

Outputs

  • Array of strings: The generated text, which can be further processed or used as desired.

Capabilities

llama-2-70b-chat-gguf is a powerful large language model with the ability to generate coherent and contextual text. The grammar-based decoding feature allows for more structured and controlled output, making it suitable for tasks that require specific formatting or templates. This model can be used for a variety of language generation tasks, such as chatbots, text summarization, and creative writing.

What can I use it for?

The llama-2-70b-chat-gguf model can be used for a wide range of natural language processing tasks, such as:

  • Chatbots and conversational AI: The model's ability to generate coherent and contextual responses makes it well-suited for building chatbots and conversational AI applications.
  • Content generation: With the grammar-based decoding feature, the model can be used to generate text that adheres to specific templates or formats, such as news articles, product descriptions, or creative writing.
  • Question answering: The model can be fine-tuned on question-answering datasets to provide relevant and informative responses to user queries.

Things to try

One interesting aspect of llama-2-70b-chat-gguf is its ability to generate text that adheres to specific grammars or JSON schemas. This can be particularly useful for tasks that require structured output, such as generating reports, filling out forms, or producing code snippets. Experimenting with different grammars and schemas can yield unique and creative results, opening up a wide range of potential applications for this model.



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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