Build multiple CPU variants and pick the best

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

docs/development.md

@@ -76,6 +76,22 @@ go build .
 
 ROCm requires elevated privileges to access the GPU at runtime.  On most distros you can add your user account to the `render` group, or run as root.
 
+#### Advanced CPU Settings
+
+By default, running `go generate ./...` will compile a few different variations
+of the LLM library based on common CPU families and vector math capabilities,
+including a lowest-common-denominator which should run on almost any 64 bit CPU
+somewhat slowly.  At runtime, Ollama will auto-detect the optimal variation to
+load.  If you would like to build a CPU-based build customized for your
+processor, you can set `OLLAMA_CUSTOM_CPU_DEFS` to the llama.cpp flags you would
+like to use.  For example, to compile an optimized binary for an Intel i9-9880H,
+you might use:
+
+```
+OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go generate ./...
+go build .
+```
+
 #### Containerized Linux Build
 
 If you have Docker available, you can build linux binaries with `./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included.  The resulting binary is placed in `./dist`

docs/troubleshooting.md

@@ -16,7 +16,38 @@ If manually running `ollama serve` in a terminal, the logs will be on that termi
 
 Join the [Discord](https://discord.gg/ollama) 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
 
-
-* `signal: illegal instruction (core dumped)`: Ollama requires AVX support from the CPU. This was introduced in 2011 and CPUs started offering it in 2012. CPUs from before that and some lower end CPUs after that may not have AVX support and thus are not supported by Ollama. Some users have had luck with building Ollama on their machines disabling the need for AVX.
+* N/A