M3 Max is a Machine Learning BEAST. So I took it for a spin with some LLM's running locally.
I also show how to gguf quantizations with llama.cpp
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Timestamps
00:00 Intro
00:40 Build from scratch - manual
09:44 Bonus script - automated
11:21 LM Studio - one handed
Repo
github.com/ggerganov/llama.cpp/
Commands
//assuming you already have a conda environment set up, and dev tools installed (see videos above for instructions)
Part 1 - manual
brew install git-lfs
git lfs install
git clone github.com/ggerganov/llama.cpp
cd llama.cpp
pip install -r requirements.txt
make
git clone huggingface.co/teknium/OpenHe... openhermes-7b-v2.5
mv openhermes-7b-v2.5 models/
python3 convert.py ./models/openhermes-7b-v2.5 --outfile ./models/openhermes-7b-v2.5/ggml-model-f16.gguf --outtype f16
./quantize ./models/openhermes-7b-v2.5/ggml-model-f16.gguf ./models/openhermes-7b-v2.5/ggml-model-q8_0.gguf q8_0
./quantize ./models/openhermes-7b-v2.5/ggml-model-f16.gguf ./models/openhermes-7b-v2.5/ggml-model-q4_k.gguf q4_k
./batched-bench ./models/openhermes-7b-v2.5/ggml-model-f16.gguf 4096 0 99 0 2048 128,512 1,2,3,4
./server -m models/openhermes-7b-v2.5/ggml-model-q4_k.gguf --port 8888 --host 0.0.0.0 --ctx-size 10240 --parallel 4 -ngl 99 -n 512
Part 2 - auto
bash -c "$(curl -s ggml.ai/server-llm.sh)"
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M2 Max 16" MacBook Pro 64GB/2TB
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