docs: generalize

Signed-off-by: Will Johnson <mwjohnson728@gmail.com>

Author: willmj

README.md

@@ -904,8 +904,8 @@ Notes:
           - if False, Scatter MoE Kernels with complete replication of experts across ranks.
       - lora tuning with ScatterMoE is supported, but because of inference restrictions on vLLM/vanilla PEFT, the expert layers and router linear layer should not be trained as `target_modules` for models being tuned with ScatterMoE. Users have control over which `target_modules` they wish to train:
           - At this time, only attention layers are trainable when using LoRA with scatterMoE. Until support for the router linear layer is added in, target modules must be specified explicitly (i.e `target_modules: ["q_proj", "v_proj", "o_proj", "k_proj"]`) instead of passing `target_modules: ["all-linear"]`.
-      - When lora tuning with ScatterMoE, the value `--fast_moe True` is not expected to work, as FSDP must be enabled when lora tuning. Run either `--fast_moe False` or `--fast-moe x>=1`.
-    - `world_size` must be divisible by the `--ep_degree`
+      - FSDP must be used when lora tuning with `--fast_moe`
+    - `world_size` must be divisible by the `ep_degree`
     - `number of experts` in the MoE module must be divisible by the `ep_degree`
     - Running fast moe modifies the state dict of the model, and must be post-processed which happens automatically and the converted checkpoint can be found at `hf_converted_checkpoint` folder within every saved checkpoint directory. Alternatively, we can perform similar option manually through [checkpoint utils](https://github.com/foundation-model-stack/fms-acceleration/blob/main/plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py) script.
       - The typical usecase for this script is to run: