undo post_process_vLLm flag

Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>

README.md

@@ -665,8 +665,6 @@ The `fms_acceleration.cli` can do more to search for all available configs, plug
 ## Inference
 Currently, we do *not* offer inference support as part of the library, but we provide a standalone script for running inference on tuned models for testing purposes. For a full list of options run `python scripts/run_inference.py --help`. Note that no data formatting / templating is applied at inference time.
 
-If you are trying to run LoRA inference on vLLM, set the `--post_process_vllm` flag to `True`.
-
 ### Running a single example
 If you want to run a single example through a model, you can pass it with the `--text` flag.
 

tuning/sft_trainer.py

@@ -523,7 +523,6 @@ def parse_arguments(parser, json_config=None):
         ) = parser.parse_dict(json_config, allow_extra_keys=True)
         peft_method = json_config.get("peft_method")
         exp_metadata = json_config.get("exp_metadata")
-        post_process_vllm = json_config.get("post_process_vllm")
     else:
         (
             model_args,
@@ -543,7 +542,6 @@ def parse_arguments(parser, json_config=None):
 
         peft_method = additional.peft_method
         exp_metadata = additional.exp_metadata
-        post_process_vllm = additional.post_process_vllm
 
     if peft_method == "lora":
         tune_config = lora_config
@@ -564,7 +562,6 @@ def parse_arguments(parser, json_config=None):
         fusedops_kernels_config,
         attention_and_distributed_packing_config,
         exp_metadata,
-        post_process_vllm,
     )