more nits

llms/mlx_lm/LORA.md

@@ -76,26 +76,13 @@ 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
+#### Prompt Masking
-There are custom functions for masking the sequence of tokens associated with the `prompt` in a completion dataset
-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.
-
-This functionality expects a ```response_template``` parameter in the configuration that is either a string representing
-a [string that indicate the start of the model's response](https://huggingface.co/docs/transformers/en/chat_templating#what-are-generation-prompts) 
-or its corresopnding tokens.  This is used to create the mask that excludes the tokens associated from the rest of
-the sequence from loss calculations.  For example (ChatML):
-
-```yaml
-response_template: "<|im_start|>assistant"
-```
-
-or (for the corresponding tokens of Gemma's response template)
-
-```yaml
-response_template: [106, 2516]
-```
 
+The default training computes a loss for every token in the sample. You can
+ignore the prompt and compute loss for just the completion by passing
+`--mask-prompt`. Note this is only supported for `chat` and `completion`
+datasets. For `chat` datasets the final message in the message list is
+considered the completion. See the [dataset section](#Data) for more details. 
 
 ### Evaluate
 

llms/mlx_lm/tuner/datasets.py

@@ -185,6 +185,8 @@ def load_hf_dataset(
 def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
     import datasets
 
+    mask_prompt = getattr(args, "mask_prompt", False)
+
     def create_hf_dataset(
         dataset_name,
         text_feature,
@@ -201,7 +203,7 @@ def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
         )
         if prompt_feature and completion_feature:
             return CompletionsDataset(
-                data, tokenizer, prompt_feature, completion_feature, mask_prompt
+                ds, tokenizer, prompt_feature, completion_feature, mask_prompt
             )
         elif chat_feature:
             return ChatDataset(

llms/mlx_lm/tuner/trainer.py

@@ -83,16 +83,6 @@ def default_loss(model, batch, lengths):
     return ce, ntoks
 
 
-def contains(small_list: List, big_list: List) -> Tuple[int, int]:
-    """
-    Returns the beginning and end index of the first occurrence of small_list in big_list.
-    """
-    small_list_length = len(small_list)
-    for ind in (i for i, e in enumerate(big_list) if e == small_list[0]):
-        if big_list[ind : ind + small_list_length] == small_list:
-            return ind, ind + small_list_length - 1
-
-
 def iterate_batches(
     dataset,
     tokenizer,