Update README.md

README.md

@@ -33,16 +33,18 @@ Haliax's documentation is available at [haliax.readthedocs.io](https://haliax.re
 
 ## Features
 
-* **Distributed Training**: We support distributed training on TPUs (and soon, GPUs), including FSDP and tensor parallelism.
+* **Distributed Training**: We support distributed training on TPUs and GPUs, including FSDP and tensor parallelism.
 * **Compatibility**: Levanter supports importing and exporting models to/from the Hugging Face ecosystem, including tokenizers, datasets, and models via [SafeTensors](https://github.com/huggingface/safetensors).
 * **Performance**: Levanter's performance rivals commercially-backed frameworks like MosaicML's Composer or Google's MaxText.
+* **Resilience**: Levanter supports fast, distributed checkpointing and fast resume from checkpoints with no data seek, making Levanter robust to preemption and hardware failure.
 * **Cached On-Demand Data Preprocessing**: We preprocess corpora online, but we cache the results of preprocessing so
 that resumes are much faster and so that subsequent runs are even faster. As soon as the first part of the cache is complete, Levanter will start training.
-* **Optimization**: Levanter supports the new [Sophia](https://arxiv.org/abs/2305.14342) optimizer, which can be 2x as fast as Adam. We also support ses [Optax](https://github.com/deepmind/optax) for optimization with AdamW, etc.
-* **Logging**: Levanter supports a few different logging backends, including [WandB](https://wandb.ai/site) and [TensorBoard](https://www.tensorflow.org/tensorboard). (Adding a new logging backend is easy!) Levanter even exposes the ability
+* **Logging**: Levanter logs a rich and detailed set of metrics covering loss and performance. Levanter also supports a few different logging backends, including [WandB](https://wandb.ai/site) and [TensorBoard](https://www.tensorflow.org/tensorboard). (Adding a new logging backend is easy!) Levanter even exposes the ability
 to log inside of JAX `jit`-ted functions.
 * **Reproducibility**: On TPU, Levanter is bitwise deterministic, meaning that the same configuration will always produce the same results, even in the face of preemption and resumption.
 * **Distributed Checkpointing**: Distributed checkpointing is supported via Google's [TensorStore](https://google.github.io/tensorstore/) library. Training can even be resumed on a different number of hosts, though this breaks reproducibility for now.
+* * **Optimization**: Levanter supports the new [Sophia](https://arxiv.org/abs/2305.14342) optimizer, which can be 2x as fast as Adam. We also support ses [Optax](https://github.com/deepmind/optax) for optimization with AdamW, etc.
+* * **Flexible**: Levanter supports tuning data mixtures without having to retokenize or shuffle data.
 
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