Add detailed guide for implementing new models in Ollama's Go inference engine.
The guide walks through the full process from initial setup to deployment, including architecture overview,
file structure, conversion process, and testing requirements. This will help new contributors understand how to add models to Ollama.
* add build to .dockerignore
* test: only build one arch
* add build to .gitignore
* fix ccache path
* filter amdgpu targets
* only filter if autodetecting
* Don't clobber gpu list for default runner
This ensures the GPU specific environment variables are set properly
* explicitly set CXX compiler for HIP
* Update build_windows.ps1
This isn't complete, but is close. Dependencies are missing, and it only builds the "default" preset.
* build: add ollama subdir
* add .git to .dockerignore
* docs: update development.md
* update build_darwin.sh
* remove unused scripts
* llm: add cwd and build/lib/ollama to library paths
* default DYLD_LIBRARY_PATH to LD_LIBRARY_PATH in runner on macOS
* add additional cmake output vars for msvc
* interim edits to make server detection logic work with dll directories like lib/ollama/cuda_v12
* remove unncessary filepath.Dir, cleanup
* add hardware-specific directory to path
* use absolute server path
* build: linux arm
* cmake install targets
* remove unused files
* ml: visit each library path once
* build: skip cpu variants on arm
* build: install cpu targets
* build: fix workflow
* shorter names
* fix rocblas install
* docs: clean up development.md
* consistent build dir removal in development.md
* silence -Wimplicit-function-declaration build warnings in ggml-cpu
* update readme
* update development readme
* llm: update library lookup logic now that there is one runner (#8587)
* tweak development.md
* update docs
* add windows cuda/rocm tests
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* llama: wire up builtin runner
This adds a new entrypoint into the ollama CLI to run the cgo built runner.
On Mac arm64, this will have GPU support, but on all other platforms it will
be the lowest common denominator CPU build. After we fully transition
to the new Go runners more tech-debt can be removed and we can stop building
the "default" runner via make and rely on the builtin always.
* build: Make target improvements
Add a few new targets and help for building locally.
This also adjusts the runner lookup to favor local builds, then
runners relative to the executable, and finally payloads.
* Support customized CPU flags for runners
This implements a simplified custom CPU flags pattern for the runners.
When built without overrides, the runner name contains the vector flag
we check for (AVX) to ensure we don't try to run on unsupported systems
and crash. If the user builds a customized set, we omit the naming
scheme and don't check for compatibility. This avoids checking
requirements at runtime, so that logic has been removed as well. This
can be used to build GPU runners with no vector flags, or CPU/GPU
runners with additional flags (e.g. AVX512) enabled.
* Use relative paths
If the user checks out the repo in a path that contains spaces, make gets
really confused so use relative paths for everything in-repo to avoid breakage.
* Remove payloads from main binary
* install: clean up prior libraries
This removes support for v0.3.6 and older versions (before the tar bundle)
and ensures we clean up prior libraries before extracting the bundle(s).
Without this change, runners and dependent libraries could leak when we
update and lead to subtle runtime errors.
Docker uses the container filesystem for name resolution, so we can't guide users
to use the name of the host group. Instead they must specify the numeric ID.
This will no longer error if built with regular gcc on windows. To help
triage issues that may come in related to different compilers, the runner now
reports the compier used by cgo.
