add stub example for finetuning, based on train-text-from-scratch
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add_subdirectory(benchmark)
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add_subdirectory(benchmark)
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add_subdirectory(baby-llama)
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add_subdirectory(baby-llama)
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add_subdirectory(train-text-from-scratch)
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add_subdirectory(train-text-from-scratch)
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add_subdirectory(finetune)
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add_subdirectory(simple)
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add_subdirectory(simple)
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add_subdirectory(embd-input)
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add_subdirectory(embd-input)
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if (LLAMA_METAL)
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if (LLAMA_METAL)
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examples/finetune/CMakeLists.txt
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examples/finetune/CMakeLists.txt
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set(TARGET finetune)
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add_executable(${TARGET} finetune.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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examples/finetune/README.md
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examples/finetune/README.md
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# train-text-from-scratch
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Basic usage instructions:
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```bash
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# get training data
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wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
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# train
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./bin/train-text-from-scratch \
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--vocab-model ../models/ggml-vocab.bin \
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--ctx 64 --embd 256 --head 8 --layer 16 \
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--checkpoint-in chk-shakespeare-256x16.bin \
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--checkpoint-out chk-shakespeare-256x16.bin \
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--model-out ggml-shakespeare-256x16-f32.bin \
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--train-data "shakespeare.txt" \
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-t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
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--print-details-interval 0 --predict 16 --use-flash
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# predict
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./bin/main -m ggml-shakespeare-256x16-f32.bin
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```
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2788
examples/finetune/finetune.cpp
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2788
examples/finetune/finetune.cpp
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