60 lines
No EOL
1.8 KiB
Markdown
60 lines
No EOL
1.8 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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When you run Ollama in a container, the logs go to stdout/stderr in the container:
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```shell
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docker logs <container-name>
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```
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(Use `docker ps` to find the container name)
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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 |