Add finnetuing process configuration to model

spanish_medica_llm.py

@@ -647,7 +647,6 @@ def configAndRunFineTuning(basemodel, dataset, eval_dataset, tokenizer):
                 evaluation_strategy = "steps", # Evaluate the model every logging step
                 eval_steps = 50,               # Evaluate and save checkpoints every 50 steps
                 do_eval = True,                # Perform evaluation at the end of training
-                eval_steps=50,
                 save_total_limit=2,
                 remove_unused_columns = True,
                 report_to = None,           # Comment this out if you don't want to use weights & baises
@@ -660,16 +659,16 @@ def configAndRunFineTuning(basemodel, dataset, eval_dataset, tokenizer):
                      model=basemodel,
                      train_dataset = dataset,
                      eval_dataset = eval_dataset,
-                    peft_config = getLoraConfiguration(),
-                    dataset_text_field = "raw_text",
-                    max_seq_length = 1024, #512
-                    tokenizer = tokenizer,
-                    args = training_args,
-                    dataset_kwargs={
+                     peft_config = getLoraConfiguration(),
+                     dataset_text_field = "raw_text",
+                     max_seq_length = 1024, #512
+                     tokenizer = tokenizer,
+                     args = training_args,
+                     dataset_kwargs={
                         "add_special_tokens": False,  # We template with special tokens
                         "append_concat_token": False, # No need to add additional separator token
-                    },
-                    packing=True
+                     },
+                     packing=True
                 )
         basemodel.config.use_cache = False  # silence the warnings. Please re-enable for inference!
         trainer.train()