change default rope to auto for v2 configs

config_templates/gretel/tasks/tabular_ft__default.yaml

@@ -30,11 +30,12 @@ task:
         # Scale the base LLM's context length by this factor
         # using RoPE scaling to handle datasets with more
         # columns, or datasets containing groups with more
-        # than a few records. You can try increasing the
-        # rope_scaling_factor (you could first try the value 2)
-        # if you hit an error for maximum tokens. It must be
-        # an integer value. The default is 1 and maximum is 6. 
-        rope_scaling_factor: 1
+        # than a few records. If set to 'auto', we will
+        # estimate a value that's enough to cover your
+        # dataset. Try increasing this value if you hit an
+        # error for maximum tokens. It must be an integer
+        # value between 1 and 6.
+        rope_scaling_factor: auto
 
     generate:
       num_records: 1000

config_templates/gretel/tasks/tabular_ft__differential_privacy.yaml

@@ -38,11 +38,12 @@ task:
         # Scale the base LLM's context length by this factor
         # using RoPE scaling to handle datasets with more
         # columns, or datasets containing groups with more
-        # than a few records. You can try increasing the
-        # rope_scaling_factor (you could first try the value 2)
-        # if you hit an error for maximum tokens. It must be
-        # an integer value. The default is 1 and maximum is 6. 
-        rope_scaling_factor: 1
+        # than a few records. If set to 'auto', we will
+        # estimate a value that's enough to cover your
+        # dataset. Try increasing this value if you hit an
+        # error for maximum tokens. It must be an integer
+        # value between 1 and 6.
+        rope_scaling_factor: auto
 
         # You can try increasing this until you run out-of-memory.
         batch_size: 4

config_templates/gretel/workflows/tabular-ft-dp.yml

@@ -46,11 +46,12 @@ steps:
           # Scale the base LLM's context length by this factor
           # using RoPE scaling to handle datasets with more
           # columns, or datasets containing groups with more
-          # than a few records. You can try increasing the
-          # rope_scaling_factor (you could first try the value 2)
-          # if you hit an error for maximum tokens. It must be
-          # an integer value. The default is 1 and maximum is 6. 
-          rope_scaling_factor: 1
+          # than a few records. If set to 'auto', we will
+          # estimate a value that's enough to cover your
+          # dataset. Try increasing this value if you hit an
+          # error for maximum tokens. It must be an integer
+          # value between 1 and 6.
+          rope_scaling_factor: auto
 
           # You can try increasing this until you run out-of-memory.
           batch_size: 4

config_templates/gretel/workflows/tabular-ft.yml

@@ -38,11 +38,12 @@ steps:
           # Scale the base LLM's context length by this factor
           # using RoPE scaling to handle datasets with more
           # columns, or datasets containing groups with more
-          # than a few records. You can try increasing the
-          # rope_scaling_factor (you could first try the value 2)
-          # if you hit an error for maximum tokens. It must be
-          # an integer value. The default is 1 and maximum is 6. 
-          rope_scaling_factor: 1
+          # than a few records. If set to 'auto', we will
+          # estimate a value that's enough to cover your
+          # dataset. Try increasing this value if you hit an
+          # error for maximum tokens. It must be an integer
+          # value between 1 and 6.
+          rope_scaling_factor: auto
 
       generate:
         num_records: 1000