Fine-Tune Qwen3 with LoRA for AI Cold Email Outreach
An AI cold email engine does one thing: it reads what you know about a company and writes a personalized outreach email — automatically, at scale. If you've ever spent an afternoon manually tweaking 50 nearly-identical emails, you understand the problem. If you've paid for Instantly, Smartlead, or Apollo, you've already solved it — just not on your own terms.
Those SaaS tools charge $30-200/month, send your prospect list to their servers, and give you a black-box model you can't touch. You can't train it on your best-performing emails. You can't add custom quality gates. You can't run it offline. For engineers and technical founders, that's a bad deal.
This system is the alternative: a locally-run pipeline where you own every layer — model weights, scoring logic, and approval gates. The core is Qwen3-1.7B, fine-tuned with LoRA adapters on MLX (Apple's framework for M1/M2 Metal acceleration). A Rust orchestration layer drives the full batch loop: pulling company records, invoking the model, running quality filters, and surfacing emails for human review before anything sends.
The result is not a toy. On a single M1 MacBook Pro, the pipeline generates 200+ personalized emails per batch in under 10 seconds — no GPU cloud, no API latency, no per-email cost. Fine-tuning converges in under 30 minutes on the same machine.