Update PEFT Doc (#8262)

* update peft doc Signed-off-by: Chen Cui <[email protected]> * remove old prompt learning doc and notebook Signed-off-by: Chen Cui <[email protected]> * fix table Signed-off-by: Chen Cui <[email protected]> * fix table Signed-off-by: Chen Cui <[email protected]> * fix table Signed-off-by: Chen Cui <[email protected]> * Merge branch 'r1.23.0' into chcui/update_peft_doc Signed-off-by: Chen Cui <[email protected]> * revert accidental changes Signed-off-by: Chen Cui <[email protected]> * revert accidental changes Signed-off-by: Chen Cui <[email protected]> --------- Signed-off-by: Chen Cui <[email protected]>
NVIDIA · Feb 3, 2024 · d9f1409 · d9f1409
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diff --git a/README.rst b/README.rst
@@ -125,7 +125,7 @@ Key Features
     * `Information retrieval <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/information_retrieval.html>`_
     * `Entity Linking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/entity_linking.html>`_
     * `Dialogue State Tracking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/dialogue.html>`_
-    * `Prompt Learning <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/prompt_learning.html>`_
+    * `Parameter Efficient Finetuning (PEFT) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/peft/landing_page.html>`_
     * `NGC collection of pre-trained NLP models. <https://ngc.nvidia.com/catalog/collections/nvidia:nemo_nlp>`_
     * `Synthetic Tabular Data Generation <https://developer.nvidia.com/blog/generating-synthetic-data-with-transformers-a-solution-for-enterprise-data-challenges/>`_
 * Text-to-Speech Synthesis (TTS):

diff --git a/docs/source/nlp/nemo_megatron/peft/landing_page.rst b/docs/source/nlp/nemo_megatron/peft/landing_page.rst
@@ -12,14 +12,14 @@ fraction of the computational and storage costs.
 NeMo supports four PEFT methods which can be used with various
 transformer-based models.
 
-==================== ===== ===== ========= ==
-\                    GPT 3 NvGPT LLaMa 1/2 T5
-==================== ===== ===== ========= ==
-Adapters (Canonical) ✅    ✅    ✅        ✅
-LoRA                 ✅    ✅    ✅        ✅
-IA3                  ✅    ✅    ✅        ✅
-P-Tuning             ✅    ✅    ✅        ✅
-==================== ===== ===== ========= ==
+==================== ===== ======== ========= ====== ==
+\                    GPT 3 Nemotron LLaMa 1/2 Falcon T5
+==================== ===== ======== ========= ====== ==
+LoRA                  ✅    ✅      ✅        ✅     ✅
+P-Tuning              ✅    ✅      ✅        ✅     ✅
+Adapters (Canonical)  ✅    ✅      ✅               ✅
+IA3                   ✅    ✅      ✅               ✅
+==================== ===== ======== ========= ====== ==
 
 Learn more about PEFT in NeMo with the :ref:`peftquickstart` which provides an overview on how PEFT works
 in NeMo. Read about the supported PEFT methods

diff --git a/docs/source/nlp/nemo_megatron/peft/quick_start.rst b/docs/source/nlp/nemo_megatron/peft/quick_start.rst
@@ -62,7 +62,7 @@ Base model classes
 PEFT in NeMo is built with a mix-in class that does not belong to any
 model in particular. This means that the same interface is available to
 different NeMo models. Currently, NeMo supports PEFT for GPT-style
-models such as GPT 3, NvGPT, LLaMa 1/2 (``MegatronGPTSFTModel``), as
+models such as GPT 3, Nemotron, LLaMa 1/2 (``MegatronGPTSFTModel``), as
 well as T5 (``MegatronT5SFTModel``).
 
 Full finetuning vs PEFT
@@ -78,11 +78,13 @@ PEFT.
    trainer = MegatronTrainerBuilder(config).create_trainer()
    model_cfg = MegatronGPTSFTModel.merge_cfg_with(config.model.restore_from_path, config)
 
+   ### Training API ###
    model = MegatronGPTSFTModel.restore_from(restore_path, model_cfg, trainer) # restore from pretrained ckpt
-   + peft_cfg = LoRAPEFTConfig(model_cfg)
+   + peft_cfg = LoraPEFTConfig(model_cfg)
    + model.add_adapter(peft_cfg) 
    trainer.fit(model)  # saves adapter weights only
 
+   ### Inference API ###
    # Restore from base then load adapter API 
    model = MegatronGPTSFTModel.restore_from(restore_path, trainer, model_cfg)
    + model.load_adapters(adapter_save_path, peft_cfg)