update finetune README

examples/finetune/README.md

@@ -1,4 +1,4 @@
-# train-text-from-scratch
+# finetune
 
 Basic usage instructions:
 
@@ -6,17 +6,50 @@ 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.bin \
-        --ctx 64 --embd 256 --head 8 --layer 16 \
-        --checkpoint-in  chk-shakespeare-256x16.bin \
-        --checkpoint-out chk-shakespeare-256x16.bin \
-        --model-out ggml-shakespeare-256x16-f32.bin \
+# finetune LORA adapter
+./bin/finetune \
+        --model-base open-llama-3b-v2-q8_0.bin \
+        --checkpoint-in  chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \
+        --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \
+        --model-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \
         --train-data "shakespeare.txt" \
-        -t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
-        --print-details-interval 0 --predict 16 --use-flash
+        --save-every 10 \
+        --threads 6 --adam-iter 30 --batch 4 --ctx 64 \
+        --print-details-interval 0 --predict 0 \
+        --use-checkpointing --use-alloc \
+        --mem-lora 2 --mem-compute 1 --mem-compute0 20
 
 # predict
-./bin/main -m ggml-shakespeare-256x16-f32.bin
+./bin/main -m open-llama-3b-v2-q8_0.bin --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
 ```
+
+Finetune 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.
+
+Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
+If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
+
+To change the amount of memory for finetuning with memory allocator (`--use-alloc`, used by default), you can use `--mem-compute0 N` to specify the number of gigabytes.
+
+After training, text is generated using the trained LORA. 
+But this text prediction is not optimized as well as it is in `main`. 
+It may result in out-of-memory crash, to disable the text prediction after training use `--predict 0`.
+
+The LORA rank is configured for each model tensor type separately with these command line options:
+
+```bash
+  --rank-att-norm N          LORA rank for attention norm tensor (default 1)
+  --rank-ffn-norm N          LORA rank for feed-forward norm tensor (default 1)
+  --rank-out-norm N          LORA rank for output norm tensor (default 1)
+  --rank-tok-embd N          LORA rank for token embeddings tensor (default 4)
+  --rank-out N               LORA rank for output tensor (default 4)
+  --rank-wq N                LORA rank for wq tensor (default 4)
+  --rank-wk N                LORA rank for wk tensor (default 4)
+  --rank-wv N                LORA rank for wv tensor (default 4)
+  --rank-wo N                LORA rank for wo tensor (default 4)
+  --rank-w1 N                LORA rank for w1 tensor (default 4)
+  --rank-w2 N                LORA rank for w2 tensor (default 4)
+  --rank-w3 N                LORA rank for w3 tensor (default 4)
+```
+
+To see all available options use `finetune --help`.