codellama-7b-instruct-gguf

Maintainer: andreasjansson

Total Score

20

Last updated 5/21/2024
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Model overview

The codellama-7b-instruct-gguf is a 7 billion parameter Llama model tuned for coding and conversation, with support for grammars and JSON schema. It is similar to other CodeLlama models like codellama-7b-instruct, codellama-13b-instruct, codellama-70b-instruct, and codellama-34b-instruct, all created by Meta.

Model inputs and outputs

The codellama-7b-instruct-gguf model takes a prompt, a grammar in GBNF format or a JSON schema, and several decoding parameters as input. It then generates a list of output strings.

Inputs

  • Prompt: The text prompt to be used for generation
  • Grammar: The grammar in GBNF format to guide the generation
  • Jsonschema: The JSON schema to describe the desired output format
  • Top K: The number of top tokens to consider during sampling
  • Top P: The probability mass to consider during sampling
  • Max Tokens: The maximum number of tokens to generate
  • Temperature: The temperature to use for sampling
  • Mirostat Mode: The mirostat sampling mode to use
  • Repeat Penalty: The penalty for repeating tokens
  • Mirostat Entropy: The target entropy for mirostat sampling
  • Presence Penalty: The penalty for presence of tokens
  • Frequency Penalty: The penalty for frequency of tokens
  • Mirostat Learning Rate: The learning rate for mirostat sampling

Outputs

  • List of strings: The generated output text

Capabilities

The codellama-7b-instruct-gguf model can generate text guided by a provided grammar or JSON schema, allowing for more structured and controlled output. This makes it well-suited for tasks like code generation, structured data generation, and other applications where the output needs to conform to specific rules or constraints.

What can I use it for?

The codellama-7b-instruct-gguf model could be useful for a variety of applications, such as:

  • Code generation: Use the model to generate code snippets or entire programs based on a provided grammar or schema.
  • Data generation: Generate synthetic data that conforms to a specific JSON schema, useful for testing or data augmentation.
  • Structured text generation: Create reports, articles, or other structured text outputs guided by a grammar.

Things to try

Try experimenting with different grammars or JSON schemas to see how the model's output changes. You could also explore the various decoding parameters, such as temperature and top-k, to fine-tune the generation process. Additionally, consider combining the codellama-7b-instruct-gguf model with other AI models or tools to create more powerful and versatile applications.



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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