Llm fine-tuning for code generation

A computing system, comprising:

• at least one processor connected to at least one memory;
• one or more non-transitory computer-readable media including computer-executable instructions stored thereon that when executed by at least the processor cause the computing system to:
• access a collection of software code samples, wherein the software code samples includes intermixed sample code and human language description of the sample code;
• generate prompts to an LLM to write code that performs as described by the human language description of the sample code;
• fine-tune a large language model to generate software code based on a code generation loss function that evaluates code generated by the LLM in response to the prompts;
• generate an evaluation score for performance of the tuned large language model as a code generator based on a value of the code generation loss function for second generated code;
• and
• automatically determine to deploy the tuned large language model to a production environment for code generation in response to the evaluation score satisfying a threshold.

A computer-implemented method, comprising:

• accessing a collection of software code samples, wherein the software code samples includes intermixed sample code and human language description of the sample code;
• generating prompts to an LLM to write code that performs as described by the human language description of the sample code;
• fine-tuning a large language model to generate software code based on a code generation loss function that evaluates code generated by the LLM in response to the prompts;
• generating an evaluation score for performance of the tuned large language model as a code generator based on a value of the code generation loss function for second generated code;
• and
• automatically signal that the fine tuning of the tuned large language model is complete in response to the evaluation score satisfying a threshold.