Hamel Husain
Founder, Parlance Labs | AI Evaluation Architect & LLM Systems Consultant
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Hamel Husain. Every entry undergoes editorial source verification.
Fine-Tuning Open Source LLMs: A Hands-On Guide for Engineers
A comprehensive, practitioner-focused field guide for evaluating and fine-tuning open-source models using Axolotl, LoRA, and curated evaluation benchmarks. Covers data synthesis, loss curve diagnosis, and catastrophic forgetting mitigation.
Focuses on instruction-tuning and task adaptation; pre-training from scratch requires massive compute clusters outside the scope of this guide.
Your AI Product Needs Evals: Comprehensive Guide to Unit Testing & LLM Evaluation
Comprehensive framework for creating automated evaluation loops, synthetically augmented test sets, and LLM-as-a-judge scoring harnesses that measure production degradation before deployment.
LLM-as-a-judge evaluators can exhibit self-preference bias and positional bias if uncalibrated against human baseline judgments.