llama.cpp/examples/train-text-from-scratch
cebtenzzre 898aeca90a
llama : implement YaRN RoPE scaling (#2268)
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Jeffrey Quesnelle <jquesnelle@gmail.com>
2023-11-01 18:04:33 -04:00
..
CMakeLists.txt cmake : install targets (#2256) 2023-07-19 10:01:11 +03:00
convert-train-checkpoint-to-gguf.py gguf : general usability improvements (#3409) 2023-10-02 14:58:46 -04:00
README.md train : finetune LORA (#2632) 2023-09-28 21:40:11 +03:00
train-text-from-scratch.cpp llama : implement YaRN RoPE scaling (#2268) 2023-11-01 18:04:33 -04:00

train-text-from-scratch

Basic usage instructions:

# get training data
wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt

# train
./bin/train-text-from-scratch \
        --vocab-model ../models/ggml-vocab-llama.gguf \
        --ctx 64 --embd 256 --head 8 --layer 16 \
        --checkpoint-in  chk-shakespeare-256x16-LATEST.gguf \
        --checkpoint-out chk-shakespeare-256x16-ITERATION.gguf \
        --model-out ggml-shakespeare-256x16-f32-ITERATION.gguf \
        --train-data "shakespeare.txt" \
        -t 6 -b 16 --seed 1 --adam-iter 256 \
        --no-checkpointing

# predict
./bin/main -m ggml-shakespeare-256x16-f32.gguf

Output files will be saved every N iterations (config with --save-every N). The pattern "ITERATION" in the output filenames will be replaced with the iteration number and "LATEST" for the latest output.

To train GGUF models just pass them to --checkpoint-in FN.