The author describes creating a 340 M-parameter Llama-based model trained exclusively on English texts published before 1900. They built custom data pipelines, tokenization, base-training and fine-tuning scripts, handled deduplication and filtering of historical sources, and trained locally and on cloud GPUs for about $80. The result is a toy “Victorian” chatbot that can hallucinate and isn’t aligned for modern safety.

• Built a 340M-parameter LLaMA-style model trained exclusively on pre-1900 English texts, doing every step (data pipeline, tokenization, base-training, fine-tuning) from scratch
• Total cloud GPU cost was about $80, after prototyping a smaller 14M-parameter version locally
• Cleaning and deduplicating ~90GB of historic sources (Gutenberg, Internet Archive, British Library, Harvard) was the hardest and most time-consuming part
• The model has no hard 1900 knowledge cutoff or safety alignment, so it can hallucinate and let anachronistic facts (like Einstein) slip through

This guide shows how to use Apple Silicon and the Gemma 3 270M model to fine-tune a small language model offline in under 10 minutes. It walks through installing the uv/MLX toolchain, preparing a simple code-review dataset, and running the training on any M-chip Mac without a cloud GPU.

• Gemma 3 270M runs on ~830MB RAM and hits 150+ tokens/sec on an M3, fine-tuning in under 10 minutes via MLX's uv toolchain (as fast as 3 minutes on an M1 Air)
• A dataset of only ~60 Python code snippets paired with expert review notes was enough to teach the model to flag security issues (SQL injection, unclosed files, insecure shell commands) and style problems
• The entire workflow runs offline on Apple Silicon with no cloud GPU or rental fees required

Organizations are increasingly faced with the decision of whether to implement Retrieval-Augmented Generation (RAG) or fine-tuning for their AI initiatives. RAG connects large language models to external databases, allowing access to real-time information, reducing inaccuracies, and enhancing security and traceability. However, implementing RAG comes with its own technical challenges that require careful planning and maintenance.

• RAG connects LLMs to external data sources so responses reflect real-time information rather than static training data
• Grounding responses in retrieved data cuts down on hallucinations and improves accuracy
• RAG offers added benefits like source traceability and better security for proprietary data
• Setting up RAG requires substantial infrastructure work—data pipelines, indexing, and ongoing maintenance for quality and scalability