MM Eval tests

README.md

@@ -44,7 +44,6 @@ torchtune currently supports the following models.
 | [Code-Llama2](https://ai.meta.com/blog/code-llama-large-language-model-coding/)   | 7B, 13B, 70B [[models](torchtune/models/code_llama2/_model_builders.py), [configs](recipes/configs/code_llama2/)] |
 | [Mistral](https://huggingface.co/mistralai)   | 7B [[models](torchtune/models/mistral/_model_builders.py), [configs](recipes/configs/mistral/)] |
 | [Gemma](https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b)   | 2B, 7B [[models](torchtune/models/gemma/_model_builders.py), [configs](recipes/configs/gemma/)] |
-| [Gemma2](https://huggingface.co/docs/transformers/main/en/model_doc/gemma2)   | 2B, 9B, 27B [[models](torchtune/models/gemma2/_model_builders.py), [configs](recipes/configs/gemma2/)] |
 | [Microsoft Phi3](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3) | Mini [[models](torchtune/models/phi3/), [configs](recipes/configs/phi3/)]
 | [Qwen2](https://qwenlm.github.io/blog/qwen2/) | 0.5B, 1.5B, 7B [[models](torchtune/models/qwen2/), [configs](recipes/configs/qwen2/)]
 

docs/source/api_ref_models.rst

@@ -361,37 +361,6 @@ To download the Gemma 7B model:
     gemma.gemma_tokenizer
 
 
-gemma2 :
---------
-
-Models of size 2B, 9B, 27B from the `Gemma family <https://blog.google/technology/developers/gemma-open-models/>`_.
-
-Important: You need to request access on `Hugging Face <https://huggingface.co/google/gemma-2-2b>`__ to use this model.
-
-To download the Gemma2 2B, 9B, 27B models :
-
-.. code-block:: bash
-
-    tune download google/gemma-2-<MODEL_SIZE>b --ignore-patterns "gemma-2-<MODEL_SIZE>b.gguf"  --hf-token <HF_TOKEN>
-
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-
-    gemma2.gemma2
-    gemma2.lora_gemma2
-    gemma2.gemma2_2b
-    gemma2.lora_gemma2_2b
-    gemma2.qlora_gemma2_2b
-    gemma2.gemma2_9b
-    gemma2.lora_gemma2_9b
-    gemma2.qlora_gemma2_9b
-    gemma2.gemma2_27b
-    gemma2.lora_gemma2_27b
-    gemma2.qlora_gemma2_27b
-    gemma.gemma_tokenizer
-
 clip
 ----
 

docs/source/tutorials/memory_optimizations.rst

@@ -167,7 +167,7 @@ In addition to :ref:`reducing model and optimizer precision <glossary_precision>
 All of our recipes support lower-precision optimizers from the `torchao <https://github.com/pytorch/ao/tree/main/torchao/prototype/low_bit_optim>`_ library.
 For single device recipes, we also support `bitsandbytes <https://huggingface.co/docs/bitsandbytes/main/en/index>`_.
 
-A good place to start might be the :class:`torchao.prototype.low_bit_optim.AdamW8bit` and :class:`bitsandbytes.optim.PagedAdamW8bit` optimizers.
+A good place to start might be the :class:`torchao.prototype.low_bit_optim.torchao.AdamW8bit` and :class:`bitsandbytes.optim.PagedAdamW8bit` optimizers.
 Both reduce memory by quantizing the optimizer state dict. Paged optimizers will also offload to CPU if there isn't enough GPU memory available. In practice,
 you can expect higher memory savings from bnb's PagedAdamW8bit but higher training speed from torchao's AdamW8bit.
 
@@ -180,7 +180,7 @@ a low precision optimizer using the :ref:`cli_label`:
 .. code-block:: bash
 
   tune run <RECIPE> --config <CONFIG> \
-  optimizer=torchao.prototype.low_bit_optim.AdamW8bit
+  optimizer=torchao.prototype.low_bit_optim.torchao.AdamW8bit
 
 .. code-block:: bash