Fine-Tuning an LLM for Dynamic JSON Configuration Generation

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Hello everyone,

I have an idea that I would love to get your insights on. I’m interested in building or fine-tuning a large language model (LLM) specifically for generating configuration JSON files. Here’s the concept in detail:

Objective: I want to create a model that can generate configuration JSON files based on a provided description. The JSON files have a specific structure with predefined keys, but the values need to be dynamically generated based on the given description.

Example: Suppose I have a description that outlines the requirements and parameters for the configuration. When I feed this description into the LLM, it should output a config.json file with the correct structure. The keys in the JSON file will remain consistent, but the values will change according to the provided description.

Key Requirements:

  1. Structured Output: The JSON should have a fixed structure with predefined keys.
  2. Dynamic Values: The values within the JSON should be dynamically generated based on the input description.
  3. Fine-Tuning or Training: Guidance on whether I should fine-tune an existing LLM or train a new model from scratch for this purpose.

Questions:

  1. Has anyone attempted something similar, and what was your approach?
  2. What are the best practices for fine-tuning an LLM for such a specific task?
  3. Are there any recommended models or frameworks that are particularly suited for this type of problem?
  4. What challenges should I anticipate in this project, and how might I address them?
  5. Any tips on ensuring the generated JSON adheres strictly to the required structure?
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2

Hey, I want to ask if you already have any solution for this? I am currently also in quite similar position where I need such a dynamic JSON value configuration agent. Do you have any approaches?

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Fine-tuning an existing LLM (like GPT or T5) with example description-JSON pairs is likely the most efficient approach. Use structured prompt engineering or function calling (like OpenAI’s function calling API) to ensure strict JSON adherence. Challenges include maintaining schema consistency and avoiding hallucinations—validating outputs with a schema validator can help. Look into Hugging Face’s transformers library and OpenAI’s tools for structured generation.