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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 8 |
1 files changed, 7 insertions, 1 deletions
@@ -180,5 +180,11 @@ When running the larger models, make sure you have enough disk space to store al In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that - I don't know yet how much the quantization affects the quality of the generated text - Probably the token sampling can be improved -- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this on Apple Silicon. For now, on Linux and Windows you can use the F16 `ggml-model-f16.bin` model, but it will be much slower. +- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this + on Apple Silicon. For now, on Linux and Windows you can use the F16 `ggml-model-f16.bin` model, but it will be much + slower. +- The Accelerate framework is actually currently unused since I found that for tensors shapes typical for the Decoder, + there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simlpy don't + know how to utilize it properly. But in any case, you can even disable it with `LLAMA_NO_ACCELERATE=1 make` and the + performance will be the same, since no BLAS calls are invoked by the current implementation |