Foundation models like TimesFM forecast numerical time series zero-shot. I tested them on payment transaction volume, added holiday covariates, and learned where they help and where they hurt.

Run DeepSeek-V4-Flash on a 2x RTX Pro 6000 (96GB each) workstation using the voipmonitor/vllm:lucifer Docker image, a Blackwell-targeted vLLM fork with sm_120 kernels, FP8 KV cache, and MTP speculative decoding.

Run Qwen3.6-27B, Gemma 4 31B, and MiniMax M2.7 locally, then connect them to the Pi coding agent for local coding.

I gave Luna access to my email. Here's what happened in a single day.

Luna monitors, follows up, and takes action on her own—powered by a local LLM and a team of background agents.

Unexpected shutdowns under sustained load on RTX Pro 6000 Blackwell: fix with a small NVML fan control daemon + systemd.

Why I run local LLMs to power a multimodal event crawler

Run Claude Code with your own local MiniMax-M2.1 model using vLLM's native Anthropic API endpoint support.

Step-by-step vLLM stable/nightly install on Ubuntu 24.04 for a dual RTX Pro 6000 (96GB x2), model download workflow, and a fix for tp=2 hangs (IOMMU). Includes tested serve commands for Devstral 123B, GLM-4.5/4.6V, Qwen3 235B, MiniMax-M2, and gpt-oss-120b.

A practical guide to injecting new knowledge into LLM models through fine-tuning, using Q&A pairs generated from documentation.