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4760e7cc0b
* sync : ggml (backend v2) (wip) * sync : migrate examples and llama.cpp to dynamic graphs (wip) * sync : update tests + fix max op params to 64 ggml-ci * sync : ggml-cuda ggml-ci * llama : fix save/load state context size ggml-ci * sync : try to fix build on tvOS * sync : pass custom graph sizes in training examples * sync : update graph copies to new ggml API * sync : update sync-ggml.sh with new files * scripts : fix header in sync script * train : fix context size calculations * llama : increase inference graph size up to 4096 nodes * train : allocate grads for backward graphs * train : allocate grads for gb_tmp |
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| .. | ||
| CMakeLists.txt | ||
| convert-train-checkpoint-to-gguf.py | ||
| README.md | ||
| train-text-from-scratch.cpp | ||
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.