Add input masking for fine-tuning in documentation

Renamed the batch iteration function (iterate_delineated_batches -> iterate_completion_batches).

llms/mlx_lm/LORA.md

@@ -76,6 +76,11 @@ You can specify the output location with `--adapter-path`.
 You can resume fine-tuning with an existing adapter with
 `--resume-adapter-file <path_to_adapters.safetensors>`.
 
+### Input Masking
+There are custom functions for masking the input sequence of tokens during the loss calculation to ensure 
+the model is not being penalized for not recreating the prompt.  To fine-tune with masked input sequences,
+use the `--mask-inputs` argument.
+
 ### Evaluate
 
 To compute test set perplexity use:

llms/mlx_lm/lora.py

@@ -185,7 +185,7 @@ def train_model(
         default_loss,
         input_masked_loss,
         iterate_batches,
-        iterate_delineated_batches,
+        iterate_completion_batches,
     )
 
     model.freeze()
@@ -249,7 +249,7 @@ def train_model(
         val_dataset=valid_set,
         training_callback=training_callback,
         iterate_batches=(
-            iterate_delineated_batches if args.mask_inputs else iterate_batches
+            iterate_completion_batches if args.mask_inputs else iterate_batches
         ),
         loss=input_masked_loss if args.mask_inputs else default_loss,
     )

llms/mlx_lm/tuner/trainer.py

@@ -130,7 +130,7 @@ def input_length(
     return output_begin
 
 
-def iterate_delineated_batches(
+def iterate_completion_batches(
     dataset: CompletionsDataset,
     tokenizer: PreTrainedTokenizer,
     batch_size: int,