update readme

e2eAIOK/deltatuner/README.md

@@ -68,6 +68,7 @@ def optimize(model, tokenizer, algo: str="auto", deltatuning_args: DeltaTunerArg
             - "auto" – If the input model is mpt, the algorithm is ssf; elif the algorithm is lora
             - "lora" – use the lora algotihm
             - "ssf" – use the ssf algotithm 
+          - deltatuning_args.best_model_structure Specifies the pre-searched delta best structure so the model can be directly initilized without searching.
         - kwargs - used to initilize deltatuning_args through key=value, such as algo="lora"
     Return 
         DeltaTunerModel - a wrapper of model, which composed of the original properties/function together with adavance properties/function provided by deltatuner
@@ -80,6 +81,7 @@ def optimize(model, tokenizer, algo: str="auto", deltatuning_args: DeltaTunerArg
 Please refer to [example page](https://github.com/intel/e2eAIOK/tree/main/example) for more use cases on fine-tuning other LLMs with the help of DeltaTuner.
 
 ## Model supported matrix
+We have upload the searched delta best structure to the [conf dir](https://github.com/intel/e2eAIOK/tree/main/e2eAIOK/deltatuner/deltatuner/conf/best_structure), so that users can directly use our searched structure for directly fine-tuning by passing the `DeltaTunerArguments.best_model_structure` to the `deltatuner.optimize` function.
 
 ### Causal Language Modeling