12 Posts getaggt mit "capstone"

The final week of May 2026 (May 27–30) proved to be the grand culmination of our experimental modeling phase. After weeks of hyperparameter tuning, GPU out-of-memory crashes, and dataset revisions, our 4th Fine-Tuning cycle (fine_tuning_4) crossed the finish line.

This post reveals the technical breakthrough that resolved the chronic "over-counseling" trap, quantitative validation of our golden weight Checkpoint-4400, and the end-to-end system architecture designed for real-world deployment.

On May 21st and 22nd, the Maeumjigi Capstone team embarked on massive data generation and preparation for the 4th Fine-Tuning (fine_tuning_4).

Over the two days of May 19th and 20th, the automation of evaluation for the 3rd fine-tuning (fine_tuning_3) and the enhancement of backend/frontend features for building the actual app service proceeded in parallel.

Mid-May 2026 was supposed to be a calm interval after wrapping up our 2nd fine-tuning (fine_tuning_2). Instead, May 17th hit our team with a relentless double challenge: condensing hundreds of raw inference metrics into an automated technical report, while simultaneously battling a cryptic HTTP 401 Unauthorized cascading error across our newly integrated FastAPI and PostgreSQL backend.

Here is the complete post-mortem of how we automated our reporting and systematically neutralized the backend authentication breakdown.

On May 13th, after fixing the hardcoding bugs discovered yesterday, we finally resumed proper inference testing for the 2nd round fine-tuned models.

The 2nd round of fine-tuning (fine_tuning_2) parallel training, which was ambitiously started to correct the LoRA hyperparameter mistake (alpha/rank setup error), finally ended on May 12th!

May 7th was the day we synthesized the fine-tuning results of our 5 models (Llama3.3-70B, DSR1-70B, HCX-Omni-8B, Qwen35-9B, HCX-Text-1.5B) and proceeded with full-scale visualization and local environment setup.

April 14, 2026, was a masterclass in distributed pipeline fault tolerance. Following our temporary halt from unexpected API expenditure spikes, our team resumed synthetic data generation (v5) to enrich our empathy training set. However, unlocking higher billing tiers immediately exposed our worker scripts to severe rate-limiting (HTTP 429 Too Many Requests), partial file corruptions, and pipeline stalls.

Here is how we re-engineered the synthesis architecture into a self-healing, unmanned autonomous pipeline.

April 5, 2026, marked a decisive operational turning point for the Maeumjigi capstone project. Having completed four iterative versions of data cleansing (v1 through v4), our team needed to accomplish two critical engineering objectives:

1. Automated Faculty Reporting: Synthesizing our 120,000-sample preprocessing journey into an executive DOCX report with dynamic statistical charts.
2. Next.js 14 Frontend Scaffolding: Launching the client-side architecture capable of handling low-latency server-sent events (SSE) for streaming model responses.

This is the record of March 31, when we conducted the Android build test of the MND Convention reservation alarm app, which was the initial direction of the Maeumjigi project, while simultaneously starting the Llama 3.1 fine-tuning data preprocessing, hinting at a full transition into an AI-based project.

This is the record of March 30, when we officially started developing the 'MND Convention Reservation Alarm' application, which was the initial idea for the Maeumjigi project. Based on the coding rules we set last time, we established a detailed project plan and laid the foundation for Test-Driven Development (TDD).

March 26, 2026, marks the historic first day of our university capstone engineering project. Today laid the structural bedrock for everything to come: adopting an autonomous AI coding assistant (Claude Code), designing team collaboration protocols, and making the pivotal architectural decision that transformed a simple reservation alert tool into Maeumjigi, a specialized AI mental health counseling platform.