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docs/source/JAX_Vision_transformer.ipynb

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"- Model checkpointing and exporting with [Orbax](https://orbax.readthedocs.io/en/latest/).\n",
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"- Optimizers and learning rate scheduling with [Optax](https://optax.readthedocs.io/en/latest/).\n",
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"- Freezing model's parameters using trainable parameters filtering with examples: 1) [`flax.nnx.optimizer.Optimizer.update`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/training/optimizer.html#flax.nnx.optimizer.Optimizer.update) and 2) [example 2 on `google/flax` GitHub Issues](https://github.com/google/flax/issues/4167#issuecomment-2324245208).\n",
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"- Other computer vision tutorials using the [JAX AI Stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)."
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"- Other computer vision tutorials using the [JAX AI Stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)."
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}
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docs/source/JAX_Vision_transformer.md

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- Model checkpointing and exporting with [Orbax](https://orbax.readthedocs.io/en/latest/).
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- Optimizers and learning rate scheduling with [Optax](https://optax.readthedocs.io/en/latest/).
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- Freezing model's parameters using trainable parameters filtering with examples: 1) [`flax.nnx.optimizer.Optimizer.update`](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/training/optimizer.html#flax.nnx.optimizer.Optimizer.update) and 2) [example 2 on `google/flax` GitHub Issues](https://github.com/google/flax/issues/4167#issuecomment-2324245208).
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- Other computer vision tutorials using the [JAX AI Stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html).
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- Other computer vision tutorials using the [JAX AI Stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html).

docs/source/JAX_basic_text_classification.ipynb

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"In this tutorial we implemented from scratch a simple convolutional neural network and trained it on a text classification dataset. Trained model shows 87% classification accuracy due to its convolutional nature. Next steps to improve the metrics could be to change the model to a transformer-based architecture.\n",
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"\n",
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"- Model checkpointing and exporting using [Orbax](https://orbax.readthedocs.io/en/latest/).\n",
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"- Other NLP tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)."
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"- Other NLP tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)."
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docs/source/JAX_basic_text_classification.md

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In this tutorial we implemented from scratch a simple convolutional neural network and trained it on a text classification dataset. Trained model shows 87% classification accuracy due to its convolutional nature. Next steps to improve the metrics could be to change the model to a transformer-based architecture.
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- Model checkpointing and exporting using [Orbax](https://orbax.readthedocs.io/en/latest/).
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- Other NLP tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html).
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- Other NLP tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html).

docs/source/JAX_for_PyTorch_users.ipynb

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"\n",
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"\n",
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"Other AI/ML tutorials to check out:\n",
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"- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)\n",
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"- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)\n",
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"\n",
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"## Just-In-Time (JIT) compilation in JAX\n",
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"\n",

docs/source/JAX_for_PyTorch_users.md

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Other AI/ML tutorials to check out:
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- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)
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- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)
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## Just-In-Time (JIT) compilation in JAX
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docs/source/JAX_image_captioning.ipynb

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"In this tutorial we implemented and trained a transformer-based model for image captioning task. We used a pretrained frozen Vision Transformer encoder and trained a small decoder to predict the next token. Observed generation capabilities of the trained model are not great. Next steps could be (1) to use larger decoder, (2) to unfreeze few top encoder layers, (3) try other decoder architectures.\n",
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"\n",
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"- Freezing model's parameters using trainable parameters filtering: [example 1](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/training/optimizer.html#flax.nnx.optimizer.Optimizer.update) and [example 2](https://github.com/google/flax/issues/4167#issuecomment-2324245208).\n",
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"- Other Computer Vision tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html).\n",
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"- Other Computer Vision tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html).\n",
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"- [LLM pretraining for text generation](https://jax-ai-stack.readthedocs.io/en/latest/JAX_for_LLM_pretraining.html)."
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docs/source/JAX_image_captioning.md

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In this tutorial we implemented and trained a transformer-based model for image captioning task. We used a pretrained frozen Vision Transformer encoder and trained a small decoder to predict the next token. Observed generation capabilities of the trained model are not great. Next steps could be (1) to use larger decoder, (2) to unfreeze few top encoder layers, (3) try other decoder architectures.
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- Freezing model's parameters using trainable parameters filtering: [example 1](https://flax.readthedocs.io/en/latest/api_reference/flax.nnx/training/optimizer.html#flax.nnx.optimizer.Optimizer.update) and [example 2](https://github.com/google/flax/issues/4167#issuecomment-2324245208).
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- Other Computer Vision tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html).
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- Other Computer Vision tutorials in [jax-ai-stack](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html).
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- [LLM pretraining for text generation](https://jax-ai-stack.readthedocs.io/en/latest/JAX_for_LLM_pretraining.html).

docs/source/JAX_porting_PyTorch_model.ipynb

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"## Further reading\n",
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"\n",
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"- [Flax documentation: Core Examples](https://flax.readthedocs.io/en/latest/examples/core_examples.html)\n",
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"- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)"
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"- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)"
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docs/source/JAX_porting_PyTorch_model.md

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## Further reading
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- [Flax documentation: Core Examples](https://flax.readthedocs.io/en/latest/examples/core_examples.html)
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- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)
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- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/getting_started.html)

docs/source/index.html

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<div>
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<span><img src="./_static/ai-stack-logo.svg"/><h1>JAX AI Stack</h1></span>
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<p>Flexible, scalable components for AI research and development</p>
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<a class="button button-primary" href="./tutorials.html">Get started</a>
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<a class="button button-primary" href="./getting_started.html">Get started</a>
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<img src="./_static/images/hero.svg" class="hero-image">
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<li>ml_dtypes - NumPy dtype extensions for machine learning.</li>
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<a class="button button-primary" href="./tutorials.html">Get started</a>
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<a class="button button-primary" href="./getting_started.html">Get started</a>
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<section class="image-section">

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