Building LLMs from Scratch – Evaluation & Deployment (Part 4 Finale)
AI Development#LLM Deployment and Evaluation📝 Blog|Analyzed: Jan 3, 2026 06:31•
Published: Jan 3, 2026 03:10
•1 min read
•r/LocalLLaMAAnalysis
This article provides a practical guide to evaluating, testing, and deploying Language Models (LLMs) built from scratch. It emphasizes the importance of these steps after training, highlighting the need for reliability, consistency, and reproducibility. The article covers evaluation frameworks, testing patterns, and deployment paths, including local inference, Hugging Face publishing, and CI checks. It offers valuable resources like a blog post, GitHub repo, and Hugging Face profile. The focus on making the 'last mile' of LLM development 'boring' (in a good way) suggests a focus on practical, repeatable processes.
Key Takeaways
- •Evaluation and testing are crucial steps after LLM training.
- •The article provides practical frameworks and patterns for evaluation.
- •Deployment options include local inference and Hugging Face publishing.
- •Repeatable publishing workflows are emphasized for reliability and reproducibility.
Reference / Citation
View Original"The article focuses on making the last mile boring (in the best way)."