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kartikayk committed Mar 20, 2024
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2 changes: 1 addition & 1 deletion docs/source/examples/configs.rst
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Expand Up @@ -161,7 +161,7 @@ will list out all the locations where an error was found.

.. code-block:: bash
tune validate --config recipes/configs/alpaca_llama2_full_finetune_single_device.yaml batch_size=4
tune validate --config recipes/configs/full_finetune_single_device.yaml batch_size=4
Best practices for writing configs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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2 changes: 1 addition & 1 deletion docs/source/examples/lora_finetune.rst
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Expand Up @@ -288,7 +288,7 @@ Let's run this experiment. We can also increase alpha (in general it is good pra

.. code-block:: bash
tune --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config _ora_finetune_distributed \
tune --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config lora_finetune_distributed \
lora_attn_modules='[q_proj, k_proj, v_proj, output_proj]' \
lora_rank=32 lora_alpha=64 output_dir=./lora_experiment_1
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2 changes: 1 addition & 1 deletion recipes/README.md
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Recipes are the primary entry points for TorchTune users. These can be thought of as end-to-end pipelines for training and optionally evaluating LLMs. Each recipe consists of three components:

- **Configurable parameters**, specified through yaml configs [example](https://github.com/pytorch/torchtune/blob/main/recipes/configs/full_finetune_distributed.yaml), command-line overrides and dataclasses
- **Configurable parameters**, specified through yaml configs [example](https://github.com/pytorch/torchtune/blob/main/recipes/configs/full_finetune_distributed.yaml) and command-line overrides
- **Recipe class**, core logic needed for training, exposed to users through a set of APIs [interface](https://github.com/pytorch/torchtune/blob/main/recipes/interfaces.py)
- **Recipe script**, puts everything together including parsing and validating configs, setting up the environment, and correctly using the recipe class

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