d88c527be3
This reduces the built-in linux version to not use any vector extensions which enables the resulting builds to run under Rosetta on MacOS in Docker. Then at runtime it checks for the actual CPU vector extensions and loads the best CPU library available
1.6 KiB
How to troubleshoot issues
Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on Mac by running the command:
cat ~/.ollama/logs/server.log
On Linux systems with systemd, the logs can be found with this command:
journalctl -u ollama
If manually running ollama serve in a terminal, the logs will be on that terminal.
Join the Discord for help interpreting the logs.
LLM libraries
Ollama includes multiple LLM libraries compiled for different GPUs and CPU
vector features. Ollama tries to pick the best one based on the capabilities of
your system. If this autodetection has problems, or you run into other problems
(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
library. cpu_avx2 will perform the best, followed by cpu_avx an the slowest
but most compatible is cpu. Rosetta emulation under MacOS will work with the
cpu library.
In the server log, you will see a message that looks something like this (varies from release to release):
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
Experimental LLM Library Override
You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
You can see what features your CPU has with the following.
cat /proc/cpuinfo| grep flags | head -1
Known issues
- N/A