1. 20 May, 2025 1 commit
  2. 16 May, 2025 1 commit
  3. 14 May, 2025 1 commit
  4. 12 May, 2025 1 commit
  5. 10 May, 2025 1 commit
  6. 08 May, 2025 1 commit
  7. 06 May, 2025 1 commit
    • Daniel Hiltgen's avatar
      Move quantization to new backend (#10363) · 42481045
      Daniel Hiltgen authored
      * Move quantization logic to GGML via new backend
      
      This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.
      
      * Remove "add model quantizations"
      
      This is no longer needed now that quantization is implemented in Go+GGML code directly.
      42481045
  8. 05 May, 2025 2 commits
  9. 24 Apr, 2025 1 commit
  10. 16 Apr, 2025 1 commit
  11. 31 Mar, 2025 1 commit
    • Bruce MacDonald's avatar
      runner: clear cache when shift is not possible (#9433) · 66b25392
      Bruce MacDonald authored
      Clear KV cache when shift operation is not supported by model.
      Added KvCacheCanShift() check to handle models that can't perform cache shifts,
      falling back to full cache clear while preserving logical token history to
      maintain expected behavior when context window fills up.
      66b25392
  12. 10 Mar, 2025 1 commit
  13. 04 Mar, 2025 1 commit
    • Michael Yang's avatar
      ml/backend/ggml: consolidate system info logging · 05a01fde
      Michael Yang authored
      - output backend system info when initializing the backend. this ensures
        this information is always present without needing to be called
        explicitly
      - convert to structured logging
      - enumerate devices rather than backends since devices are ordered
      - track device indices grouped by device name
      05a01fde
  14. 28 Feb, 2025 1 commit
  15. 27 Feb, 2025 2 commits
  16. 06 Feb, 2025 1 commit
  17. 31 Jan, 2025 1 commit
  18. 30 Jan, 2025 1 commit
  19. 29 Jan, 2025 1 commit
    • Michael Yang's avatar
      next build (#8539) · dcfb7a10
      Michael Yang authored
      
      
      * 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: default avatarjmorganca <jmorganca@gmail.com>
      Co-authored-by: default avatarDaniel Hiltgen <daniel@ollama.com>
      dcfb7a10
  20. 08 Jan, 2025 1 commit
  21. 13 Dec, 2024 1 commit
  22. 11 Dec, 2024 2 commits
    • Blake Mizerany's avatar
      llama: preserve field order in user-defined JSON schemas (#8002) · 9039c821
      Blake Mizerany authored
      Previously we decoded and re-encoded JSON schemas during validation,
      which served no purpose since json.RawMessage already validates JSON
      syntax. Worse, the re-encoding lost field ordering from the original
      schema, which affects inference quality during step-by-step reasoning.
      
      While fixing this ordering issue by using json.RawMessage directly,
      testing revealed that schema_to_grammar (from llama.cpp) also fails to
      preserve field order during grammar generation. This appears to be the
      root cause of inference degradation.
      
      This change prevents us from mangling the user's original schema order,
      but we still need to address the ordering issue in schema_to_grammar.
      That will be a separate change.
      
      Updates #7978
      9039c821
    • Jeffrey Morgan's avatar
      527cc978
  23. 10 Dec, 2024 2 commits
    • Daniel Hiltgen's avatar
      Remove unused runner CpuFeatures (#8032) · b9ccb374
      Daniel Hiltgen authored
      The final implementation of #7499 removed dynamic vector requirements
      in favor of a simpler filename based model, and this was left over logic that
      is no longer needed.
      b9ccb374
    • Daniel Hiltgen's avatar
      build: Make target improvements (#7499) · 4879a234
      Daniel Hiltgen authored
      * 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.
      4879a234
  24. 05 Dec, 2024 1 commit
  25. 03 Dec, 2024 1 commit
  26. 20 Nov, 2024 1 commit
    • Jesse Gross's avatar
      runner.go: Retry decoding after defragmentation if needed · 7121dfa3
      Jesse Gross authored
      Fragmentation of the KV cache can occur due to cache shifting or
      different sequences getting processed. Decode uses a heuristic to
      decide if it should defrag. However, this heuristic isn't 100%
      accurate, so decoding can sometimes fail by surprise.
      
      For these cases, if decode indicates that there is no KV cache space,
      we should defrag and then try again.
      7121dfa3
  27. 19 Nov, 2024 1 commit
  28. 14 Nov, 2024 1 commit
  29. 12 Nov, 2024 1 commit
  30. 02 Nov, 2024 2 commits
    • Jesse Gross's avatar
      llama: Improve error handling · 312d9de1
      Jesse Gross authored
      Check for NULL return values from llama.cpp in more places and
      convert them into Go errors, which should make debugging easier
      in the future rather than having hidden surprises in our data
      structures.
      312d9de1
    • Jesse Gross's avatar
      runner.go: Only allocate 1 element embedding batches for mllama · a103dae0
      Jesse Gross authored
      Mllama has large embeddings (100 MB per image) and each embedding is
      represented as 1 token when passed to llama.cpp. Batches are pre-
      allocated for the size of the tokens times the batch size, so this
      results in allocations of over 50 GB at the default batch size.
      On some systems, these mallocs will fail.
      
      Since an image is represented as a single token and mllama doesn't
      support more than 1 image per request, we only need to allocate a
      batch size of 1, which is much more reasonable. In addition, for
      non-multimodal models, we don't need to allocate the embedding
      batches at all.
      
      Fixes #7464
      a103dae0
  31. 30 Oct, 2024 3 commits
  32. 29 Oct, 2024 1 commit
  33. 24 Oct, 2024 1 commit