llmevaluation

An eval harness and report on some of the latest LLMs that can be run locally

AI-powered email generation assistant built with Streamlit and GPT-4o-mini or gemini, featuring prompt engineering evaluation and custom metrics comparison.

LLM prompt benchmark on 150 RAGBench samples × 5 strategies. RAG: 89.5% faithfulness, 10.5% ungrounded vs zero-shot: 31.3%, 68.7%. Qwen3 + Gemini judge.

PrefRank predicts which LLM response humans prefer using a 3-class classification framework. Combines linguistic, structural, and TF-IDF features with calibrated models (MLP, RF, XGBoost, LightGBM) — no embeddings required.

Python agent that ingests funnel data, generates a written drop-off diagnosis using Claude, and includes an A/B eval of the design decision.

This repository is a practical overview of inference engineering for Large Language Model deployment, with vLLM as the serving engine. It is meant to help you understand not only how to start a model server, but also how to reason about throughput, latency, batching, quantization, KV cache memory, and GPU VRAM requirements before deploying a model.