doc fix (#3187)

docs/lora_optims.qmd

@@ -5,10 +5,11 @@ description: "Custom autograd functions and Triton kernels in Axolotl for optimi
 
 Inspired by [Unsloth](https://github.com/unslothai/unsloth), we've implemented two
 optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU
-(in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function
+(including the DDP, DeepSpeed, and FSDP2 settings) training. These include (1) SwiGLU
-Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was
+and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom
-to leverage operator fusion and tensor re-use in order to improve speed and reduce
+autograd functions. Our goal was to leverage operator fusion and tensor re-use in order
-memory usage during the forward and backward passes of these calculations.
+to improve speed and reduce memory usage during the forward and backward passes of
+these calculations.
 
 We currently support several common model architectures, including (but not limited to):
 
@@ -131,6 +132,5 @@ computation path.
 ## Future Work
 
 - Support for additional model architectures
-- Support for the FSDP setting
 - Support for dropout and bias
 - Additional operator fusions