Add implementation to cli

unsloth-cli.py

@@ -42,6 +42,7 @@ def run(args):
     from transformers import TrainingArguments
     from unsloth import is_bfloat16_supported
     import logging
+    from unsloth import RawTextDataLoader
 
     logging.getLogger("hf-to-gguf").setLevel(logging.WARNING)
 
@@ -98,6 +99,21 @@ def run(args):
             texts.append(text)
         return {"text": texts}
 
+    def load_dataset_smart(args):
+        if args.raw_text_file:
+            # Use raw text loader
+            loader = RawTextDataLoader(tokenizer, args.chunk_size, args.stride)
+            dataset = loader.load_from_file(args.raw_text_file)
+        elif args.dataset.endswith(('.txt', '.md', '.json', '.jsonl')):
+            # Auto-detect local raw text files
+            loader = RawTextDataLoader(tokenizer)
+            dataset = loader.load_from_file(args.dataset)
+        else:
+            # Existing HuggingFace dataset logic
+            dataset = load_dataset(args.dataset, split="train")
+            dataset = dataset.map(formatting_prompts_func, batched=True)
+        return dataset
+
     use_modelscope = strtobool(os.environ.get("UNSLOTH_USE_MODELSCOPE", "False"))
     if use_modelscope:
         from modelscope import MsDataset
@@ -389,5 +405,37 @@ if __name__ == "__main__":
         "--hub_token", type = str, help = "Token for pushing the model to Hugging Face hub"
     )
 
+    parser.add_argument(
+        "--raw_text_file", 
+        type=str, 
+        help="Path to raw text file for training"
+    )
+    parser.add_argument(
+        "--chunk_size", 
+        type=int, 
+        default=2048, 
+        help="Size of text chunks for training"
+    )
+    parser.add_argument(
+        "--stride", 
+        type=int, 
+        default=512, 
+        help="Overlap between chunks"
+    )
+
+    TRAINING_MODES = {
+        'instruction': 'Standard instruction-following',
+        'causal': 'Causal language modeling (raw text)',
+        'completion': 'Text completion tasks'
+    }
+
+    parser.add_argument(
+        "--training_mode",
+        type=str,
+        default="instruction", 
+        choices=list(TRAINING_MODES.keys()),
+        help="Training mode for the model"
+    )
+
     args = parser.parse_args()
     run(args)