Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
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Updated
Dec 17, 2024 - Python
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Build a Large Language Model (From Scratch) book and Finetuned Models
Unattended Lightweight Text Classifiers with LLM Embeddings
🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇
[ACL 2023] Solving Math Word Problems via Cooperative Reasoning induced Language Models (LLMs + MCTS + Self-Improvement)
Premove ITN: context-aware inverse text normalization for English voice-agent transcripts.
Lightweight prompt-injection detector built for AI agents, copilots, and LLM apps. Fast, local, calibrated.
A collection of my Kaggle competition solutions.
The implementation of DeBERTaV3-based commonsense question answering on CommonsenseQA.
Benchmarked comparison of multiple serving patterns for Transformer encoder models (BERT-style classification, NLI) on Amazon SageMaker AI — ONNX CPU, multi-container, inference component, LMI, scale-to-zero
A WAF for LLMs — pure-Rust reverse proxy that inspects, scores & filters prompts/responses to GPT/Claude: prompt-injection detection (regex+heuristics+DeBERTa ML), secret/PII masking, risk scoring, streaming, audit logging.
Key Point Analysis: implementation of two-component system for performing Key Point Matching and Key Point Generation task with multiple PLMs.
Application for training the pretrained transformer model DeBERTaV3 on an Aspect Based Sentiment Analysis task
An 184M-parameter monolingual decision engine delivering calibrated distributions from one 22ms forward pass while matching models 2.3x its size.
The code of Hierarchical Multi-label Classification (HMC). It is a final course project of Natural Language Processing and Deep Learning, 2022 Fall.
Implementing science-related multiple-choice question answering based on LLMs and RAG.
Finetuning Large Language Models
Classification of medical texts to differentiate between human medical and veterinary subjects.
Scraping paper data, preprocessed and trained using BERT variants, deployment and an integration to website
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