optionally be able to specify alpaca or chat style prompts

scripts/finetune.py

@@ -31,7 +31,7 @@ DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
 def choose_device(cfg):
     def get_device():
         if torch.cuda.is_available():
-            return "cuda"
+            return f"cuda:{cfg.local_rank}"
         else:
             try:
                 if torch.backends.mps.is_available():
@@ -131,7 +131,8 @@ def train(
     # then overwrite the value
     cfg_keys = dict(cfg).keys()
     for k in kwargs:
-        if k in cfg_keys:
+        # if not strict, allow writing to cfg even if it's not in the yml already
+        if k in cfg_keys or cfg.strict is False:
             # handle booleans
             if isinstance(cfg[k], bool):
                 cfg[k] = bool(kwargs[k])
@@ -169,6 +170,15 @@ def train(
         inference=("inference" in kwargs),
     )
 
+    if "merge_lora" in kwargs and cfg.adapter is not None:
+        print("running merge of LoRA with base model")
+        model = model.merge_and_unload()
+
+        if cfg.local_rank == 0:
+            print("saving merged model")
+            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        return
+
     if "inference" in kwargs:
         logging.info("calling do_inference function")
         do_inference(cfg, model, tokenizer)
@@ -216,6 +226,8 @@ def train(
         )
 
     logging.info("Starting trainer...")
+    if cfg.group_by_length:
+        logging.info("hang tight... sorting dataset for group_by_length")
     resume_from_checkpoint = cfg.resume_from_checkpoint
     if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
         possible_checkpoints = [