I built a doctor for local LLM setups: duplicate weights, stale templates, and Ollama silently truncating context

I run local models through Ollama and kept hitting the same quiet problems, so I wrote a scanner for them. llm-doctor checks your model stores (Ollama, LM Studio, Hugging Face, MLX) for duplicate weights, stale chat templates, outdated GGUFs and orphan blobs. It can also expose Ollama models to llama-server without re-downloading. The part that surprised me is the endpoint probe. On my MacBook Air, Ollama 0.34.4 served qwen3:1.7b with a 4,096-token window although the model supports 40,960. A 7,000-token prompt returned HTTP 200 and the model only saw the last 2,050 tokens. No error. The probe tests context, tool calls, streaming, JSON schema and prefix cache, and prints the fix. It is MIT licensed, Python 3.10 or newer, and not on PyPI yet, so install from GitHub. Tested mostly on macOS with Ollama, so Linux and Windows reports are welcome. https://github.com/Arthur031221/llm-doctor submitted by /u/Arthur122103 [link] [comments]

Source: r/ollama | 2026-09-30