Rename MistralNeMo2407Config12B to MistralNeMoConfig12B per review's …

…suggestion

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

nemo/collections/llm/__init__.py

@@ -70,7 +70,7 @@
     MaskedTokenLossReduction,
     MistralConfig7B,
     MistralModel,
-    MistralNeMo2407Config12B,
+    MistralNeMoConfig12B,
     MixtralConfig8x3B,
     MixtralConfig8x7B,
     MixtralConfig8x22B,
@@ -116,7 +116,7 @@
     "t5_forward_step",
     "MaskedTokenLossReduction",
     "MistralConfig7B",
-    "MistralNeMo2407Config12B",
+    "MistralNeMoConfig12B",
     "MistralModel",
     "MixtralConfig8x3B",
     "MixtralConfig8x7B",

nemo/collections/llm/gpt/model/__init__.py

@@ -53,7 +53,7 @@
     LlamaConfig,
     LlamaModel,
 )
-from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel, MistralNeMo2407Config12B
+from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel, MistralNeMoConfig12B
 from nemo.collections.llm.gpt.model.mixtral import (
     MixtralConfig8x3B,
     MixtralConfig8x7B,

nemo/collections/llm/gpt/model/mistral.py

@@ -59,7 +59,7 @@ class MistralConfig7B(GPTConfig):
 
 
 @dataclass
-class MistralNeMo2407Config12B(MistralConfig7B):
+class MistralNeMoConfig12B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-nemo/
     """
@@ -75,7 +75,7 @@ class MistralNeMo2407Config12B(MistralConfig7B):
 
 
 @dataclass
-class MistralNeMo2407Config123B(MistralConfig7B):
+class MistralNeMoConfig123B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-large-2407/
     """

nemo/collections/llm/recipes/mistral_nemo_12b.py

@@ -24,32 +24,32 @@
 from nemo import lightning as nl
 from nemo.collections.llm.api import finetune, pretrain
 from nemo.collections.llm.gpt.data.mock import MockDataModule
-from nemo.collections.llm.gpt.model.mistral import MistralModel, MistralNeMo2407Config12B
+from nemo.collections.llm.gpt.model.mistral import MistralModel, MistralNeMoConfig12B
 from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
 from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
 from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
 from nemo.utils.exp_manager import TimingCallback
 
-NAME = "mistral_nemo_base_2407"
+NAME = "mistral_nemo_base_12b"
 
 
 @run.cli.factory(name=NAME)
 def model() -> run.Config[pl.LightningModule]:
     """
-    Factory function to create a Mistral-Nemo-Base-2407 model configuration.
+    Factory function to create a Mistral-Nemo-Base-12B model configuration.
 
     Returns:
-        run.Config[pl.LightningModule]: Configuration for the Mistral-Nemo-Base-2407 model.
+        run.Config[pl.LightningModule]: Configuration for the Mistral-Nemo-Base-12B model.
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain model=mistral_nemo_base_2407 ...
+            $ nemo llm pretrain model=mistral_nemo_base_12b ...
 
         Python API usage:
             >>> model_config = model()
             >>> print(model_config)
     """
-    return run.Config(MistralModel, config=run.Config(MistralNeMo2407Config12B))
+    return run.Config(MistralModel, config=run.Config(MistralNeMoConfig12B))
 
 
 def trainer(
@@ -65,7 +65,7 @@ def trainer(
     callbacks: Optional[list[run.Config[Callback]]] = None,
 ) -> run.Config[nl.Trainer]:
     """
-    Configure the NeMo Lightning Trainer for Mistral-Nemo-Base-2407 model.
+    Configure the NeMo Lightning Trainer for Mistral-Nemo-Base-12B model.
 
     This function sets up the distributed training strategy and other training parameters.
 
@@ -86,7 +86,7 @@ def trainer(
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain trainer=mistral_nemo_base_2407 ...
+            $ nemo llm pretrain trainer=mistral_nemo_base_12b ...
 
         Python API usage:
             >>> trainer_config = trainer(num_nodes=2, num_gpus_per_node=8)
@@ -139,7 +139,7 @@ def pretrain_recipe(
     dir: Optional[str] = None, name: str = "default", num_nodes: int = 1, num_gpus_per_node: int = 8, fn=pretrain
 ) -> run.Partial:
     """
-    Create a pre-training recipe for Mistral-Nemo-Base-2407 model.
+    Create a pre-training recipe for Mistral-Nemo-Base-12B model.
 
     This function sets up a complete configuration for pre-training, including
     model, trainer, data, logging, optimization, and resumption settings.
@@ -156,8 +156,8 @@ def pretrain_recipe(
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain --factory mistral_nemo_base_2407
-            $ nemo llm pretrain --factory "mistral_nemo_base_2407(num_nodes=2, name='my_mistral_pretrain')"
+            $ nemo llm pretrain --factory mistral_nemo_base_12b
+            $ nemo llm pretrain --factory "mistral_nemo_base_12b(num_nodes=2, name='my_mistral_pretrain')"
 
         Python API usage:
             >>> recipe = pretrain_recipe(name="mistral_pretrain", num_nodes=2)
@@ -187,7 +187,7 @@ def pretrain_recipe(
 @run.cli.factory(name=NAME + "_hf")
 def hf_resume() -> run.Config[nl.AutoResume]:
     """
-    Configure automatic resumption from a Hugging Face checkpoint for Mistral-Nemo-Base-2407 model.
+    Configure automatic resumption from a Hugging Face checkpoint for Mistral-Nemo-Base-12B model.
 
     This function sets up the configuration to resume training from a pre-trained
     Hugging Face model checkpoint.
@@ -199,7 +199,7 @@ def hf_resume() -> run.Config[nl.AutoResume]:
 
     Note:
         This is particularly useful for fine-tuning scenarios where you want to
-        start from the pre-trained Mistral-Nemo-Base-2407 model.
+        start from the pre-trained Mistral-Nemo-Base-12B model.
     """
     return run.Config(
         nl.AutoResume, restore_config=run.Config(nl.RestoreConfig, path="hf://mistralai/Mistral-Nemo-Base-2407")