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91 lines
4.4 KiB
Markdown
91 lines
4.4 KiB
Markdown
# finetune
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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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# finetune LORA adapter
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./bin/finetune \
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--model-base open-llama-3b-v2-q8_0.gguf \
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--checkpoint-in chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \
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--checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \
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--lora-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \
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--train-data "shakespeare.txt" \
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--save-every 10 \
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--threads 6 --adam-iter 30 --batch 4 --ctx 64 \
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--use-checkpointing
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# predict
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./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
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```
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Finetune output files will be saved every N iterations (config with `--save-every N`).
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The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output.
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So in above example after 10 iterations these files will be written:
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- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf
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- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
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- lora-open-llama-3b-v2-q8_0-shakespeare-10.bin
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- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
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After 10 more iterations:
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- chk-lora-open-llama-3b-v2-q8_0-shakespeare-20.gguf
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- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
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- lora-open-llama-3b-v2-q8_0-shakespeare-20.bin
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- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
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Checkpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter.
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llama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`.
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These LORA adapters can then be used by `main` together with the base model, like in the 'predict' example command above.
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In `main` you can also load multiple LORA adapters, which will then be mixed together.
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For example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this:
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```bash
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./bin/main -m open-llama-3b-v2-q8_0.gguf \
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--lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \
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--lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin
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```
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You can change how strong each LORA adapter is applied to the base model by using `--lora-scaled FN SCALE` instead of `--lora FN`.
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For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one:
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```bash
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./bin/main -m open-llama-3b-v2-q8_0.gguf \
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--lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \
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--lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \
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--lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
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```
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The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
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Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
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If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
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The default LORA rank can be specified with `--lora-r N`.
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The LORA rank can be configured for each model tensor type separately with these command line options:
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```bash
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--lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default 4)
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--rank-att-norm N LORA rank for attention norm tensor (default 1)
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--rank-ffn-norm N LORA rank for feed-forward norm tensor (default 1)
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--rank-out-norm N LORA rank for output norm tensor (default 1)
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--rank-tok-embd N LORA rank for token embeddings tensor (default 4)
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--rank-out N LORA rank for output tensor (default 4)
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--rank-wq N LORA rank for wq tensor (default 4)
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--rank-wk N LORA rank for wk tensor (default 4)
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--rank-wv N LORA rank for wv tensor (default 4)
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--rank-wo N LORA rank for wo tensor (default 4)
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--rank-w1 N LORA rank for w1 tensor (default 4)
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--rank-w2 N LORA rank for w2 tensor (default 4)
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--rank-w3 N LORA rank for w3 tensor (default 4)
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```
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The LORA rank of 'norm' tensors should always be 1.
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To see all available options use `finetune --help`.
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