removed distributed training detail from llm readme.

Author: kohankhaki

templates/src/llm/README.md

@@ -3,22 +3,4 @@
 This directory includes templates for language-model workloads:
 
 - [text_classification](text_classification/): fine-tunes a small LLM on AG News via Hugging Face Trainer.
-- [finetune_distributed](finetune_distributed/): distributed finetuning example adapted from VectorLM (https://github.com/VectorInstitute/vectorlm).
-
-## Finetune Distributed (DDP/FSDP)
-
-Run the distributed template:
-```bash
-uv run python -m llm.finetune_distributed.launch \
-  compute=bon_echo/a40_4x \
-  +trainer.dist.mode=fsdp --multirun
-```
-You can choose **DDP** or **FSDP** mode by setting the `+trainer.dist.mode` argument (`ddp` or `fsdp`).
-
-A few points to clarify for this template:
-- **`launch.py`** is the Hydra entrypoint; it merges config layers and hands the resolved config to Submitit.
-- **`distributed_launcher.py`** is a Submitit helper; it shells out to `torch.distributed.run` so that torchrun controls per-rank workers without re-entering Hydra (the same pattern used in VectorLM).
-- **`train.py`** is the torchrun worker; it loads the saved config, builds tokenizer, dataloaders, model, and optimizer, and then delegates to the Trainer.
-- **`trainer_core.py`** is a minimal trainer (adapted from VectorLM’s `trainer.py`); it handles gradient accumulation, checkpointing, optional evaluation, and works with either DDP or FSDP.
-
-Hydra and Submitit resolve and submit jobs once. Torchrun (DDP/FSDP) needs to own process creation per GPU. Launching `torch.distributed.run` in a subprocess is the standard Hydra + Submitit approach: it avoids nested Hydra invocations, keeps the Hydra run directory stable for requeues and checkpoints, and makes local debugging under `torchrun` straightforward.
+- [finetune_distributed](finetune_distributed/): distributed finetuning template using DDP and FSDP.