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| -rw-r--r-- | README.md | 7 |
1 files changed, 6 insertions, 1 deletions
@@ -22,6 +22,11 @@ The main goal is to run the model using 4-bit quantization on a MacBook. - Runs on the CPU This was hacked in an evening - I have no idea if it works correctly. +Please do not make conclusions about the models based on the results from this implementation. +For all I know, it can be completely wrong. This project is for educational purposes and is not going to be maintained properly. +New features will probably be added mostly through community contributions, if any. + +--- Here is a typical run using LLaMA-7B: @@ -183,7 +188,7 @@ When running the larger models, make sure you have enough disk space to store al - 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, +- The Accelerate framework is actually currently unused since I found that for tensor 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 |