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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
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1.6 KiB
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53 lines
No EOL
1.6 KiB
Markdown
# How to troubleshoot issues
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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:
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```shell
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cat ~/.ollama/logs/server.log
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```
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On Linux systems with systemd, the logs can be found with this command:
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```shell
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journalctl -u ollama
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```
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If manually running `ollama serve` in a terminal, the logs will be on that terminal.
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Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
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## LLM libraries
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Ollama includes multiple LLM libraries compiled for different GPUs and CPU
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vector features. Ollama tries to pick the best one based on the capabilities of
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your system. If this autodetection has problems, or you run into other problems
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(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
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library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
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but most compatible is `cpu`. Rosetta emulation under MacOS will work with the
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`cpu` library.
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In the server log, you will see a message that looks something like this (varies
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from release to release):
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```
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Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
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```
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**Experimental LLM Library Override**
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You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
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autodetection, so for example, if you have a CUDA card, but want to force the
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CPU LLM library with AVX2 vector support, use:
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```
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OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
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```
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You can see what features your CPU has with the following.
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```
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cat /proc/cpuinfo| grep flags | head -1
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```
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## Known issues
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* N/A |