Generalize prompt_feature and completion_feature for use in local datasets to facilitate compatibility with many other training dataset formats.

llms/mlx_lm/LORA.md

@@ -247,8 +247,18 @@ Refer to the documentation for the model you are fine-tuning for more details.
 {"text": "This is an example for the model."}
 ```
 
-Note, the format is automatically determined by the dataset. Note also, keys in
+Note, the format is automatically determined by the dataset. 
-each line not expected by the loader will be ignored.
+
+For the completion data format, a different key can be used for the _prompt_ and for the _completion_ by specifying 
+the following, for example, in the YAML config:
+
+```yaml
+prompt_feature: "input"
+completion_feature: "output"
+```
+
+Here, `input` is now the expected key instead of "prompt" and `output` is the expected key instead of "completion". 
+Note also, keys in each line not expected by the loader will be ignored.
 
 > [!NOTE]
 > Each example in the datasets must be on a single line. Do not put more than
@@ -270,7 +280,7 @@ Otherwise, provide a mapping of keys in the dataset to the features MLX LM
 expects. Use a YAML config to specify the Hugging Face dataset arguments. For
 example:
 
-```
+```yaml
 hf_dataset:
   name: "billsum"
   prompt_feature: "text"

llms/mlx_lm/lora.py

@@ -61,6 +61,8 @@ CONFIG_DEFAULTS = {
     "config": None,
     "grad_checkpoint": False,
     "lr_schedule": None,
+    "prompt_feature": "prompt",
+    "completion_feature": "completion",
     "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
 }
 

llms/mlx_lm/tuner/datasets.py

@@ -81,12 +81,20 @@ class CompletionsDataset:
         return len(self._data)
 
 
-def create_dataset(data, tokenizer: PreTrainedTokenizer):
+<<<<<<< HEAD
+def create_dataset(
+    data,
+    tokenizer: PreTrainedTokenizer,
+    prompt_feature: Optional[str] = None,
+    completion_feature: Optional[str] = None,
+):
     sample = data[0]
+    prompt_feature = prompt_feature or "prompt"
+    completion_feature = completion_feature or "completion"
 
     if "messages" in sample:
         return ChatDataset(data, tokenizer)
-    elif "prompt" in sample and "completion" in sample:
+    elif prompt_feature in sample and completion_feature in sample:
         return CompletionsDataset(data, tokenizer)
     elif "text" in sample:
         return Dataset(data, tokenizer)
@@ -97,20 +105,30 @@ def create_dataset(data, tokenizer: PreTrainedTokenizer):
         )
 
 
-def load_local_dataset(data_path: Path, tokenizer: PreTrainedTokenizer):
+def load_local_dataset(
+    data_path: Path,
+    tokenizer: PreTrainedTokenizer,
+    prompt_feature: str = None,
+    completion_feature: str = None,
+):
     def load_subset(path):
         if not path.exists():
             return []
         with open(path, "r") as fid:
             data = [json.loads(l) for l in fid]
-        return create_dataset(data, tokenizer)
+        return create_dataset(data, tokenizer, prompt_feature, completion_feature)
 
     names = ("train", "valid", "test")
     train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names]
     return train, valid, test
 
 
-def load_hf_dataset(data_id: str, tokenizer: PreTrainedTokenizer):
+def load_hf_dataset(
+    data_id: str,
+    tokenizer: PreTrainedTokenizer,
+    prompt_feature: str = None,
+    completion_feature: str = None,
+):
     from datasets import exceptions, load_dataset
 
     try:
@@ -119,7 +137,13 @@ def load_hf_dataset(data_id: str, tokenizer: PreTrainedTokenizer):
         names = ("train", "valid", "test")
 
         train, valid, test = [
-            create_dataset(dataset[n], tokenizer) if n in dataset.keys() else []
+            (
+                create_dataset(
+                    dataset[n], tokenizer, prompt_feature, completion_feature
+                )
+                if n in dataset.keys()
+                else []
+            )
             for n in names
         ]
 
@@ -176,10 +200,14 @@ def load_dataset(args, tokenizer: PreTrainedTokenizer):
     else:
         data_path = Path(args.data)
         if data_path.exists():
-            train, valid, test = load_local_dataset(data_path, tokenizer)
+            train, valid, test = load_local_dataset(
+                data_path, tokenizer, args.prompt_feature, args.completion_feature
+            )
         else:
             print(f"Loading Hugging Face dataset {args.data}.")
-            train, valid, test = load_hf_dataset(args.data, tokenizer)
+            train, valid, test = load_hf_dataset(
+                args.data, tokenizer, args.prompt_feature, args.completion_feature
+            )
 
     if args.train and len(train) == 0:
         raise ValueError(