The primary goal of this model is to extract structured biographical information from unstructured narrative text. It acts as a specialized Information Extraction engine that parses natural language descriptions and outputs standardized JSON objects containing key entities.
Project case study
LLM Fine-Tuning for Information Extraction (NER)
This project provides a pipeline for fine-tuning the Phi-3-mini-4k-instruct model for Information Extraction tasks. By leveraging the Unsloth framework and LoRA (Low-Rank Adaptation), we enable efficient fine-tuning on consumer-grade GPUs, transforming unstructured text into structured JSON data.
Key outcomes
- Efficient Fine-Tuning: Uses Unsloth's optimized kernels for 2x faster training and 60% less memory usage.
- Configurable Pipeline: All training parameters (learning rate, epochs, LoRA rank) are externalized in config.yaml.
- GGUF Export: Automatically exports the fine-tuned model to GGUF format for easy inference with tools like Ollama or llama.cpp.
- Reproducible: Python script and requirements file provided for consistent environment setup.
- Entity Extraction: Identifies and extracts specific entities such as Name, Age, Job, Gender.