diff --git a/comps/finetuning/README.md b/comps/finetuning/README.md
index 6f554ca221..83b2adddc5 100644
--- a/comps/finetuning/README.md
+++ b/comps/finetuning/README.md
@@ -114,7 +114,42 @@ curl http://${your_ip}:8015/v1/fine_tuning/jobs \
   }'
 ```
 
-#### 3.2.2 Reranking Model Training
+#### 3.2.2 Instruction Tuning with SQFT's Neural Low-Rank Adapter Search (NLS)
+
+In addition to traditional fine-tuning, you can use SQFT's NLS to fine-tune your model.
+More details about SQFT can be found in [this paper](https://aclanthology.org/2024.findings-emnlp.749.pdf).
+Please follow the additional installation requirements [here](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/opea#-start-the-nls-microservice-with-python).
+Use the following command to launch a finetuning job with the NLS algorithm:
+
+```bash
+# create a fine-tuning job with NLS
+# Max LoRA rank: 16
+#   LoRA target modules            -> Low-rank search space
+#   ["q_proj", "k_proj", "v_proj"] -> [16,12,8]
+#   ["up_proj"]                    -> [16,12,8]
+#   ["down_proj"]                  -> [16,12,8]
+curl http://${your_ip}:8015/v1/fine_tuning/jobs \
+  -X POST \
+  -H "Content-Type: application/json" \
+  -d '{
+    "training_file": "alpaca_data.json",
+    "model": "meta-llama/Llama-2-7b-chat-hf",
+    "General": {
+      "lora_config": {
+        "r": 16,
+        "neural_lora_search": true,
+        "target_module_groups": [["q_proj", "k_proj", "v_proj"], ["up_proj"], ["down_proj"]],
+        "search_space": ["16,12,8", "16,12,8", "16,12,8"]
+      }
+    }
+  }'
+```
+
+Detailed explanations for the parameters can be found [here](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/opea#create-an-nls-fine-tuning-job).
+Additional use-cases and benefits of SQFT are available [here](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/opea).
+Instructions to extracting the desired sub-adapter and merging it with the base model can be found [here](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/opea#leverage-the-fine-tuned-super-adapter).
+
+#### 3.2.3 Reranking Model Training
 
 Use the following command to launch a finetuning job for reranking model finetuning, such as `BAAI/bge-reranker-large`:
 
@@ -133,7 +168,7 @@ curl http://${your_ip}:8015/v1/fine_tuning/jobs \
   }'
 ```
 
-#### 3.2.3 Embedding Model Training
+#### 3.2.4 Embedding Model Training
 
 Use the following command to launch a finetuning job for embedding model finetuning, such as `BAAI/bge-base-en-v1.5`:
 
@@ -173,7 +208,7 @@ curl http://${your_ip}:8015/v1/fine_tuning/jobs \
 
 ```
 
-#### 3.2.4 LLM Pretraining
+#### 3.2.5 LLM Pretraining
 
 Use the following command to launch a job for LLM pretraining, such as `meta-llama/Llama-2-7b-hf`:
 
@@ -199,7 +234,7 @@ Below is an example for the format of the pretraining dataset:
 {"text": "A boy with a blue tank top sitting watching three dogs."}
 ```
 
-#### 3.2.5 Direct Preference Optimization (DPO)
+#### 3.2.6 Direct Preference Optimization (DPO)
 
 Use the following command to launch a job for LLM Direct Preference Optimization, such as `meta-llama/Llama-2-7b-hf`:
 
diff --git a/comps/finetuning/finetune_config.py b/comps/finetuning/finetune_config.py
index 0b2faf53db..1cf3b08794 100644
--- a/comps/finetuning/finetune_config.py
+++ b/comps/finetuning/finetune_config.py
@@ -5,9 +5,9 @@
 
 from typing import List, Optional, Union
 
-from pydantic import BaseModel, Field, validator
+from pydantic import BaseModel, Field, root_validator, validator
 
-from comps.cores.proto.api_protocol import FineTuningJobsRequest
+from comps.cores.proto.api_protocol import FineTuningJobIDRequest, FineTuningJobsRequest
 
 PRECISION_BF16 = "bf16"
 PRECISION_FP16 = "fp16"
@@ -37,6 +37,24 @@ class LoraConfig(BaseModel):
     target_modules: Optional[List[str]] = None
 
 
+class SQFTNLSConfig(LoraConfig):
+    neural_lora_search: bool = False
+    target_module_groups: Optional[List[List[str]]] = None
+    search_space: Optional[List[str]] = None
+
+    @root_validator(pre=True)
+    def set_target_modules(cls, values):
+        if values.get("neural_lora_search"):
+            target_module_groups = values.get("target_module_groups")
+            search_space = values.get("search_space")
+            if target_module_groups is None or search_space is None:
+                raise ValueError("Please specified `target_module_groups` and `search_space` when using NLS strategy.")
+            if len(search_space) != len(target_module_groups):
+                raise ValueError("The length of `search_space` must be equal to the length of `target_module_groups`.")
+            values["target_modules"] = [module for groups in target_module_groups for module in groups]
+        return values
+
+
 class GeneralConfig(BaseModel):
     base_model: str = None
     tokenizer_name: Optional[str] = None
@@ -47,7 +65,7 @@ class GeneralConfig(BaseModel):
     resume_from_checkpoint: Optional[str] = None
     save_strategy: str = "no"
     config: LoadConfig = LoadConfig()
-    lora_config: Optional[LoraConfig] = LoraConfig()
+    lora_config: Optional[Union[LoraConfig, SQFTNLSConfig]] = LoraConfig()
     enable_gradient_checkpointing: bool = False
     task: str = "instruction_tuning"
 
@@ -200,3 +218,12 @@ class FineTuningParams(FineTuningJobsRequest):
     General: GeneralConfig = GeneralConfig()
     Dataset: DatasetConfig = DatasetConfig()
     Training: TrainingConfig = TrainingConfig()
+
+
+class ExtractSubAdapterParams(FineTuningJobIDRequest):
+    adapter_version: str = "heuristic"
+    custom_config: Optional[List[int]] = None
+
+
+class MergeAdapterParams(FineTuningJobIDRequest):
+    adapter_version: Optional[str] = None
diff --git a/comps/finetuning/finetuning_service.py b/comps/finetuning/finetuning_service.py
index 64097c720c..1d76eab0ae 100644
--- a/comps/finetuning/finetuning_service.py
+++ b/comps/finetuning/finetuning_service.py
@@ -4,12 +4,14 @@
 
 from comps import opea_microservices, register_microservice
 from comps.cores.proto.api_protocol import FineTuningJobIDRequest, UploadFileRequest
-from comps.finetuning.finetune_config import FineTuningParams
+from comps.finetuning.finetune_config import ExtractSubAdapterParams, FineTuningParams, MergeAdapterParams
 from comps.finetuning.handlers import (
     handle_cancel_finetuning_job,
     handle_create_finetuning_jobs,
+    handle_extract_sub_adapter,
     handle_list_finetuning_checkpoints,
     handle_list_finetuning_jobs,
+    handle_merge_adapter,
     handle_retrieve_finetuning_job,
     handle_upload_training_files,
     upload_file,
@@ -63,5 +65,17 @@ def list_checkpoints(request: FineTuningJobIDRequest):
     return checkpoints
 
 
+@register_microservice(
+    name="opea_service@finetuning", endpoint="/v1/finetune/extract_sub_adapter", host="0.0.0.0", port=8015
+)
+def extract_sub_adapter(request: ExtractSubAdapterParams):
+    return handle_extract_sub_adapter(request)
+
+
+@register_microservice(name="opea_service@finetuning", endpoint="/v1/finetune/merge_adapter", host="0.0.0.0", port=8015)
+def merge_adapter(request: MergeAdapterParams):
+    return handle_merge_adapter(request)
+
+
 if __name__ == "__main__":
     opea_microservices["opea_service@finetuning"].start()
diff --git a/comps/finetuning/handlers.py b/comps/finetuning/handlers.py
index a47b9f980a..fb4fe9afdc 100644
--- a/comps/finetuning/handlers.py
+++ b/comps/finetuning/handlers.py
@@ -11,7 +11,7 @@
 from typing import Dict
 
 from fastapi import BackgroundTasks, File, Form, HTTPException, UploadFile
-from pydantic_yaml import to_yaml_file
+from pydantic_yaml import parse_yaml_file_as, to_yaml_file
 from ray.job_submission import JobSubmissionClient
 
 from comps import CustomLogger
@@ -23,7 +23,12 @@
     FineTuningJobList,
     UploadFileRequest,
 )
-from comps.finetuning.finetune_config import FinetuneConfig, FineTuningParams
+from comps.finetuning.finetune_config import (
+    ExtractSubAdapterParams,
+    FinetuneConfig,
+    FineTuningParams,
+    MergeAdapterParams,
+)
 
 logger = CustomLogger("finetuning_handlers")
 
@@ -134,6 +139,98 @@ def handle_create_finetuning_jobs(request: FineTuningParams, background_tasks: B
     return job
 
 
+def handle_extract_sub_adapter(request: ExtractSubAdapterParams):
+    fine_tuning_job_id = request.fine_tuning_job_id
+    finetune_config_file = f"{JOBS_PATH}/{fine_tuning_job_id}.yaml"
+    finetune_config = parse_yaml_file_as(FinetuneConfig, finetune_config_file)
+
+    job = running_finetuning_jobs.get(fine_tuning_job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail=f"Fine-tuning job '{fine_tuning_job_id}' not found!")
+    finetuned_model_path = os.path.join(OUTPUT_DIR, fine_tuning_job_id)
+    assert finetuned_model_path == finetune_config.General.output_dir
+    if not os.path.exists(finetuned_model_path):
+        raise HTTPException(
+            status_code=404,
+            detail=f"The fine-tuned model saved by the fine-tuning job '{fine_tuning_job_id}' was not found!",
+        )
+    if job.status != "succeeded":
+        raise HTTPException(status_code=404, detail=f"Fine-tuning job '{fine_tuning_job_id}' has not completed!")
+
+    if finetune_config.General.lora_config is None:
+        raise HTTPException(
+            status_code=404,
+            detail=f"The fine-tuning job '{fine_tuning_job_id}' does not enable LoRA adapter fine-tuning!",
+        )
+    if not finetune_config.General.lora_config.neural_lora_search:
+        raise HTTPException(
+            status_code=404,
+            detail=f"The fine-tuning job '{fine_tuning_job_id}' did not enable NLS algorithm, "
+            f"there is no need to extract sub-adapters!",
+        )
+    nncf_config_path = os.path.join(finetune_config.General.output_dir, "nncf_config.json")
+    if not os.path.exists(nncf_config_path):
+        raise HTTPException(
+            status_code=404, detail=f"The NNCF config file does not exist in the fine-tuning job '{fine_tuning_job_id}!"
+        )
+
+    from comps.finetuning.utils.extract_sub_adapter import main as extract_sub_adapter_main
+
+    extract_sub_adapter_main(
+        adapter_model_path=finetuned_model_path,
+        nncf_config=nncf_config_path,
+        adapter_version=request.adapter_version,
+        custom_config=request.custom_config,
+    )
+
+    return fine_tuning_job_id
+
+
+def handle_merge_adapter(request: MergeAdapterParams):
+    fine_tuning_job_id = request.fine_tuning_job_id
+    finetune_config_file = f"{JOBS_PATH}/{fine_tuning_job_id}.yaml"
+    finetune_config = parse_yaml_file_as(FinetuneConfig, finetune_config_file)
+
+    job = running_finetuning_jobs.get(fine_tuning_job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail=f"Fine-tuning job '{fine_tuning_job_id}' not found!")
+    finetuned_model_path = os.path.join(OUTPUT_DIR, fine_tuning_job_id)
+    assert finetuned_model_path == finetune_config.General.output_dir
+    if not os.path.exists(finetuned_model_path):
+        raise HTTPException(
+            status_code=404,
+            detail=f"The fine-tuned model saved by the fine-tuning job '{fine_tuning_job_id}' was not found!",
+        )
+    if job.status != "succeeded":
+        raise HTTPException(status_code=404, detail=f"Fine-tuning job '{fine_tuning_job_id}' has not completed!")
+
+    if finetune_config.General.lora_config is None:
+        raise HTTPException(
+            status_code=404,
+            detail=f"The fine-tuning job '{fine_tuning_job_id}' does not enable LoRA adapter fine-tuning!",
+        )
+
+    adapter_path = finetuned_model_path
+    adapter_version = request.adapter_version
+    if adapter_version is not None:
+        adapter_path = os.path.join(adapter_path, adapter_version)
+        if not os.path.exists(adapter_path):
+            raise HTTPException(
+                status_code=404,
+                detail=f"The fine-tuning job '{fine_tuning_job_id}' does not have a '{adapter_version}' adapter!",
+            )
+
+    from comps.finetuning.utils.merge_adapter import main as merge_adapter_main
+
+    merge_adapter_main(
+        base_model_path=finetune_config.General.base_model,
+        adapter_model_path=adapter_path,
+        output_path=os.path.join(adapter_path, "merged_model"),
+    )
+
+    return fine_tuning_job_id
+
+
 def handle_list_finetuning_jobs():
     finetuning_jobs_list = FineTuningJobList(data=list(running_finetuning_jobs.values()), has_more=False)
 
diff --git a/comps/finetuning/llm_on_ray/finetune/finetune.py b/comps/finetuning/llm_on_ray/finetune/finetune.py
index d105269a40..5216cc660b 100644
--- a/comps/finetuning/llm_on_ray/finetune/finetune.py
+++ b/comps/finetuning/llm_on_ray/finetune/finetune.py
@@ -39,6 +39,18 @@
 
 logger = CustomLogger("llm_on_ray/finetune")
 
+try:
+    from nncf.experimental.torch.nas.bootstrapNAS.training.model_creator_helpers import (
+        create_compressed_model_from_algo_names,
+    )
+    from nncf.torch.model_creation import create_nncf_network
+
+    from comps.finetuning.utils.create_sqft_nncf_config import create_sqft_nncf_config
+
+    is_nncf_available = True
+except ImportError:
+    is_nncf_available = False
+
 
 def adapt_transformers_to_device(config: Dict):
     device = config["Training"]["device"]
@@ -338,6 +350,7 @@ def load_model(config: Dict):
     model_config = config["General"].get("config", {})
     task = config["General"].get("task", "instruction_tuning")
     ref_model = None
+    nls_controller = None
     if task in ["instruction_tuning", "pretraining", "dpo"]:
         model = transformers.AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=model_dtype, **model_config)
         if task == "dpo":
@@ -346,8 +359,21 @@ def load_model(config: Dict):
             )
         lora_config = config["General"].get("lora_config", None)
         if lora_config and task == "instruction_tuning":
+            neural_lora_search = lora_config.pop("neural_lora_search", False)
+            target_module_groups = lora_config.pop("target_module_groups", None)
+            search_space = lora_config.pop("search_space", None)
             peft_config = LoraConfig(**lora_config)
             model = get_peft_model(model, peft_config)
+
+            # Neural LoRA Search (NLS)
+            if neural_lora_search:
+                if not is_nncf_available:
+                    raise NotImplementedError("NNCF is not installed. Please install it for enabling NLS algorithm.")
+                nncf_config = create_sqft_nncf_config(
+                    config=config, model=model, target_module_groups=target_module_groups, search_space=search_space
+                )
+                model = create_nncf_network(model, nncf_config)
+                nls_controller, model = create_compressed_model_from_algo_names(model, nncf_config, algo_names=["nls"])
     elif task == "rerank":
         model = CrossEncoder.from_pretrained(
             config["Dataset"].get("train_group_size", 8),
@@ -383,10 +409,10 @@ def load_model(config: Dict):
 
     model.to(dtype=model_dtype, device=torch.device(config["Training"]["device"]))
 
-    return model, ref_model
+    return model, ref_model, nls_controller
 
 
-def get_trainer(config: Dict, model, ref_model, tokenizer, tokenized_dataset, data_collator):
+def get_trainer(config: Dict, model, ref_model, tokenizer, tokenized_dataset, data_collator, nls_controller=None):
     device = config["Training"]["device"]
     task = config["General"].get("task", "instruction_tuning")
     if device in ["cpu", "gpu", "cuda"]:
@@ -411,18 +437,23 @@ def get_trainer(config: Dict, model, ref_model, tokenizer, tokenized_dataset, da
                 max_length=config["Dataset"].get("max_length", 1024),
             )
         else:
-            trainer = Trainer(
-                model=model,
-                args=training_args,
-                train_dataset=tokenized_dataset["train"],
-                eval_dataset=(
+            trainer_args = {
+                "model": model,
+                "args": training_args,
+                "train_dataset": tokenized_dataset["train"],
+                "eval_dataset": (
                     tokenized_dataset["validation"] if tokenized_dataset.get("validation") is not None else None
                 ),
-                tokenizer=tokenizer,
-                data_collator=data_collator,
-            )
+                "tokenizer": tokenizer,
+                "data_collator": data_collator,
+            }
+            if nls_controller is not None:
+                trainer_args["compression_ctrl"] = nls_controller
+            trainer = Trainer(**trainer_args)
         return training_args, trainer
     elif device in ["hpu"]:
+        if nls_controller is not None:
+            raise NotImplementedError("NLS algorithm is not supported on HPU now.")
         from optimum.habana import GaudiConfig
         from optimum.habana.transformers import GaudiTrainer, GaudiTrainingArguments
 
@@ -495,9 +526,11 @@ def train_func(config: Dict[str, Any]):
 
     data_collator = prepare_data_collator(config, tokenizer)
 
-    model, ref_model = load_model(config)
+    model, ref_model, nls_controller = load_model(config)
 
-    training_args, trainer = get_trainer(config, model, ref_model, tokenizer, tokenized_dataset, data_collator)
+    training_args, trainer = get_trainer(
+        config, model, ref_model, tokenizer, tokenized_dataset, data_collator, nls_controller=nls_controller
+    )
 
     logger.info("train start")
     trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
diff --git a/comps/finetuning/utils/create_sqft_nncf_config.py b/comps/finetuning/utils/create_sqft_nncf_config.py
new file mode 100644
index 0000000000..731791da41
--- /dev/null
+++ b/comps/finetuning/utils/create_sqft_nncf_config.py
@@ -0,0 +1,130 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+
+try:
+    from nncf import NNCFConfig
+    from nncf.experimental.torch import sqft
+
+    is_nncf_available = True
+except ImportError:
+    is_nncf_available = False
+
+
+NNCF_CONFIG_TEMPLATE = {
+    "input_info": [
+        {"sample_size": [1, 256], "type": "long", "keyword": "input_ids"},
+        {"sample_size": [1, 256], "type": "long", "keyword": "attention_mask"},
+    ],
+    "SQFT": {
+        "training": {
+            "algorithm": "nls",
+            "elasticity": {
+                "available_elasticity_dims": ["width"],
+                "width": {"overwrite_groups": [], "overwrite_groups_widths": []},
+            },
+        }
+    },
+}
+
+
+def add_lr_epochs(nncf_config, learning_rate=3e-4, num_train_epochs=3):
+    """Add learning rate and epochs to the NNCF configuration.
+
+    Args:
+        nncf_config (dict): The NNCF configuration dictionary.
+        learning_rate (float): The initial learning rate to set.
+        num_epochs (int): The number of epochs to set.
+
+    Returns:
+        dict: The updated NNCF configuration.
+    """
+    overwrite_groups_widths = nncf_config["SQFT"]["training"]["elasticity"]["width"]["overwrite_groups_widths"]
+    # Add learning rate and epochs to the configuration
+    nncf_config["SQFT"]["training"]["schedule"] = {
+        "list_stage_descriptions": [
+            {
+                "train_dims": ["width"],
+                "width_indicator": max([len(widths) for widths in overwrite_groups_widths]),
+                "init_lr": learning_rate,
+                "epochs": num_train_epochs,
+                "epochs_lr": num_train_epochs,
+            }
+        ]
+    }
+    return nncf_config
+
+
+def get_model_paths(model, target_module_name):
+    """Find all paths to the target layer in the model.
+
+    Args:
+        model (torch.nn.Module): The model to search.
+        target_module_name (str): The name of the target layer.
+
+    Returns:
+        list: A list of paths to the target layer.
+    """
+
+    def find_layers(module, target_module_name, path, paths):
+        for name, sub_module in module.named_children():
+            new_path = f"{path}/{sub_module.__class__.__name__}[{name}]"
+            if target_module_name in name:
+                # Check if 'lora_A' is in the sub_module's children
+                for sub_name, _ in sub_module.named_children():
+                    if "lora_A" in sub_name:
+                        paths.append(f"{new_path}/ModuleDict[lora_A]/NNCFLinear[default]/linear_0")
+            find_layers(sub_module, target_module_name, new_path, paths)
+
+    base_path = model.__class__.__name__
+    paths = []
+    find_layers(model, target_module_name, base_path, paths)
+    return paths
+
+
+def create_sqft_nncf_config(config, model, target_module_groups=None, search_space=None):
+    """Load and preprocess the NNCF configuration file.
+
+    Returns:
+        NNCFConfig: The preprocessed NNCF configuration object.
+    """
+    if not is_nncf_available:
+        raise NotImplementedError("NNCF is not installed. Please install it for enabling NLS algorithm.")
+    if search_space is None and target_module_groups:
+        raise ValueError("Neural LoRA search is enabled, `search_space` and `target_module_groups` must be provided.")
+    # The NNCF Config will be automatically generated based on `target_module_groups` and `search_space`.
+    num_hidden_layers = model.config.num_hidden_layers
+    nncf_config_dict = NNCF_CONFIG_TEMPLATE
+    overwrite_groups = []
+    for group in target_module_groups:
+        group_paths = []
+        for module in group:
+            target_layer_name = module
+            paths = get_model_paths(model, target_layer_name)
+            assert paths, f"No paths found for module {module}"
+            group_paths.append(paths)
+        # Transpose the list of lists to combine paths by their positions
+        transposed_paths = list(zip(*group_paths))
+        overwrite_groups.extend([list(path_group) for path_group in transposed_paths])
+    nncf_config_dict["SQFT"]["training"]["elasticity"]["width"]["overwrite_groups"] = overwrite_groups
+
+    overwrite_groups_widths = []
+    for space in search_space:
+        space = [int(width) for width in space.split(",")]
+        overwrite_groups_widths.extend([space] * num_hidden_layers)
+    nncf_config_dict["SQFT"]["training"]["elasticity"]["width"]["overwrite_groups_widths"] = overwrite_groups_widths
+    assert len(overwrite_groups) == len(overwrite_groups_widths)
+    nncf_config_dict = add_lr_epochs(
+        nncf_config_dict,
+        learning_rate=config["Training"]["learning_rate"],
+        num_train_epochs=config["Training"]["epochs"],
+    )
+    nncf_config = NNCFConfig.from_dict(nncf_config_dict)
+
+    nncf_config["log_dir"] = config["General"]["output_dir"]
+    os.makedirs(nncf_config["log_dir"], exist_ok=True)
+    with open(os.path.join(nncf_config["log_dir"], "nncf_config.json"), "w") as f:
+        json.dump(nncf_config, f, indent=4)
+    return nncf_config
diff --git a/comps/finetuning/utils/extract_sub_adapter.py b/comps/finetuning/utils/extract_sub_adapter.py
new file mode 100644
index 0000000000..2f5eccde32
--- /dev/null
+++ b/comps/finetuning/utils/extract_sub_adapter.py
@@ -0,0 +1,101 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import re
+import shutil
+
+import torch
+from peft.utils import CONFIG_NAME, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME
+
+try:
+    from nncf import NNCFConfig
+
+    is_nncf_available = True
+except ImportError:
+    is_nncf_available = False
+
+
+PATTERN = re.compile(r"[[](.*?)[]]", re.S)
+
+
+def get_width_for_query_prefix(torch_module_to_width, query_module, length=5):
+    """Get the width for a given query module prefix.
+
+    Args:
+        torch_module_to_width (dict): Mapping from torch module to width.
+        query_module (str): The query module name.
+        length (int, optional): The length of the prefix to match. Default is 5.
+
+    Returns:
+        int: The width for the query module prefix.
+    """
+    query_module_list = query_module.split(".")
+    width = next(
+        (
+            value
+            for torch_module, value in torch_module_to_width.items()
+            if torch_module.split(".")[:length] == query_module_list[:length]
+        ),
+        None,
+    )
+    return width
+
+
+def main(adapter_model_path, nncf_config, adapter_version, custom_config=None):
+    if not is_nncf_available:
+        raise NotImplementedError("NNCF is not installed. Please install it.")
+    output_dir = os.path.join(adapter_model_path, adapter_version)
+    os.makedirs(output_dir, exist_ok=True)
+    nncf_config = NNCFConfig.from_json(nncf_config)
+    try:
+        groups = nncf_config["SQFT"]["training"]["elasticity"]["width"]["overwrite_groups"]
+        groups_widths = nncf_config["SQFT"]["training"]["elasticity"]["width"]["overwrite_groups_widths"]
+        assert len(groups) == len(groups_widths)
+    except Exception:
+        raise ValueError("Cannot get the search space in NNCF config.")
+
+    if adapter_version == "maximal":
+        subnetwork_config = {idx: space[0] for idx, space in enumerate(groups_widths)}
+    elif adapter_version == "heuristic":
+        subnetwork_config = {idx: space[(len(space) - 1) // 2] for idx, space in enumerate(groups_widths)}
+    elif adapter_version == "minimal":
+        subnetwork_config = {idx: space[-1] for idx, space in enumerate(groups_widths)}
+    else:
+        assert custom_config is not None, "Missing custom subnetwork config."
+        assert isinstance(custom_config, list), "Custom config must be a list."
+        subnetwork_config = {i: value for i, value in enumerate(custom_config)}
+
+    # Mapping: nncf node -> width
+    nncf_node_to_width = {}
+    for idx, value in subnetwork_config.items():
+        space = groups_widths[idx]
+        assert min(space) <= value <= max(space)
+        cur_dict = {node: value for node in groups[idx]}
+        nncf_node_to_width.update(cur_dict)
+
+    # Prune adapter model (LoRA low-rank)
+    lora_torch_module_to_width = {
+        ".".join(re.findall(PATTERN, k)): v for k, v in nncf_node_to_width.items() if "lora_A" in k
+    }
+    num_module_name_item = list(lora_torch_module_to_width.keys())[0].split(".").index("lora_A")
+    # Load adapter weights
+    try:
+        super_adapter_weights = torch.load(os.path.join(adapter_model_path, WEIGHTS_NAME))
+    except:
+        from safetensors.torch import load_file
+
+        super_adapter_weights = load_file(os.path.join(adapter_model_path, SAFETENSORS_WEIGHTS_NAME))
+    sub_adapter_weights = {}
+    for weight_key, weight_tensor in super_adapter_weights.items():
+        width = get_width_for_query_prefix(lora_torch_module_to_width, weight_key, length=num_module_name_item)
+        if width is not None:
+            is_loraA = "lora_A" in weight_key
+            new_weight_tensor = weight_tensor[:width].clone() if is_loraA else weight_tensor[:, :width].clone()
+        else:
+            new_weight_tensor = weight_tensor.clone()
+        sub_adapter_weights[weight_key] = new_weight_tensor
+    os.makedirs(output_dir, exist_ok=True)
+    torch.save(sub_adapter_weights, os.path.join(output_dir, WEIGHTS_NAME))
+    config_path = os.path.join(adapter_model_path, CONFIG_NAME)
+    shutil.copy(config_path, output_dir)
diff --git a/comps/finetuning/utils/merge_adapter.py b/comps/finetuning/utils/merge_adapter.py
new file mode 100644
index 0000000000..44fd01e8ad
--- /dev/null
+++ b/comps/finetuning/utils/merge_adapter.py
@@ -0,0 +1,18 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import intel_extension_for_pytorch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+def main(base_model_path, adapter_model_path, output_path):
+    base_model = AutoModelForCausalLM.from_pretrained(base_model_path, trust_remote_code=True)
+    model = PeftModel.from_pretrained(base_model, adapter_model_path)
+    model.eval()
+    merged_model = model.merge_and_unload()
+    merged_model.train(False)
+    base_model.save_pretrained(output_path, state_dict=merged_model.state_dict())
+
+    tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
+    tokenizer.save_pretrained(output_path)
diff --git a/tests/finetuning/test_finetuning.sh b/tests/finetuning/test_finetuning.sh
index 11a544dfda..1dcb6d7b60 100644
--- a/tests/finetuning/test_finetuning.sh
+++ b/tests/finetuning/test_finetuning.sh
@@ -22,6 +22,19 @@ function build_docker_images() {
     fi
 }
 
+function build_sqft_docker_images() {
+    cd $WORKPATH
+    echo $(pwd)
+    curl -o comps/finetuning/Dockerfile.sqft https://raw.githubusercontent.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/main/SQFT/opea/Dockerfile
+    docker build --no-cache -t opea/finetuning:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg HF_TOKEN=$HF_TOKEN -f comps/finetuning/Dockerfile.sqft .
+    if [ $? -ne 0 ]; then
+        echo "opea/finetuning (sqft) built fail"
+        exit 1
+    else
+        echo "opea/finetuning (sqft) built successful"
+    fi
+}
+
 function start_service() {
     export no_proxy="localhost,127.0.0.1,"${ip_address}
     docker run -d --name="test-comps-finetuning-server" -p $finetuning_service_port:$finetuning_service_port -p $ray_port:$ray_port --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy opea/finetuning:comps
@@ -119,6 +132,35 @@ function validate_finetune() {
     done
 }
 
+function validate_merge_or_extract_adapter() {
+    local URL="$1"
+    local SERVICE_NAME="$2"
+    local DOCKER_NAME="$3"
+    local EXPECTED_DATA="$4"
+    local INPUT_DATA="$5"
+
+    HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' -d "$INPUT_DATA" "$URL")
+    HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
+    RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
+
+    if [ "$HTTP_STATUS" -ne "200" ]; then
+        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        docker logs $DOCKER_NAME >> ${LOG_PATH}/finetuning-server_merge_or_extract_adapter.log
+        exit 1
+    else
+        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
+    fi
+
+    # Check if the parsed values match the expected values
+    if [[ "$RESPONSE_BODY" != *"$EXPECTED_DATA"* ]]; then
+        echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
+        docker logs $DOCKER_NAME >> ${LOG_PATH}/finetuning-server_merge_or_extract_adapter.log
+        exit 1
+    else
+        echo "[ $SERVICE_NAME ] Content is as expected."
+    fi
+}
+
 function validate_microservice() {
     cd $LOG_PATH
     export no_proxy="localhost,127.0.0.1,"${ip_address}
@@ -225,6 +267,84 @@ EOF
 
 }
 
+function validate_sqft_microservice() {
+    cd $LOG_PATH
+    export no_proxy="localhost,127.0.0.1,"${ip_address}
+
+    ##########################
+    #      general test      #
+    ##########################
+    # test /v1/dataprep upload file
+    echo '[{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."},{"instruction": "Give three tips for staying healthy.", "input": "", "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."}]' > $LOG_PATH/test_data.json
+    validate_upload \
+        "http://${ip_address}:$finetuning_service_port/v1/files" \
+        "general - upload" \
+        "test-comps-finetuning-server" \
+        "fine-tune" \
+        "test_data.json"
+
+    # test /v1/fine_tuning/jobs (LoRA)
+    validate_finetune \
+        "http://${ip_address}:$finetuning_service_port/v1/fine_tuning/jobs" \
+        "general - finetuning" \
+        "test-comps-finetuning-server" \
+        '{"id":"ft-job' \
+        '{"training_file": "test_data.json","model": "facebook/opt-125m", "General": {"lora_config": {"r": 8, "target_modules": ["q_proj"]}}}'
+
+    # test merging the LoRA adapter into the base model
+    validate_merge_or_extract_adapter \
+        "http://${ip_address}:$finetuning_service_port/v1/finetune/merge_adapter" \
+        "adapter merge" \
+        "test-comps-finetuning-server" \
+        "${FINTUNING_ID}" \
+        "{\"fine_tuning_job_id\": \"${FINTUNING_ID}\"}"
+
+
+    ##########################
+    #        sqft test       #
+    ##########################
+    # test /v1/fine_tuning/jobs (SQFT-NLS)
+    validate_finetune \
+        "http://${ip_address}:$finetuning_service_port/v1/fine_tuning/jobs" \
+        "sqft - finetuning" \
+        "test-comps-finetuning-server" \
+        '{"id":"ft-job' \
+        '{"training_file": "test_data.json","model": "facebook/opt-125m", "General": {"lora_config": {"r": 8, "neural_lora_search": true, "target_module_groups": [["q_proj"]], "search_space": ["8,6,4"]}}}'
+
+    # test extracting heuristic sub-adapter
+    validate_merge_or_extract_adapter \
+        "http://${ip_address}:$finetuning_service_port/v1/finetune/extract_sub_adapter" \
+        "extract heuristic sub-adapter" \
+        "test-comps-finetuning-server" \
+        "${FINTUNING_ID}" \
+        "{\"fine_tuning_job_id\": \"${FINTUNING_ID}\", \"adapter_version\": \"heuristic\"}"
+
+    # test merging the heuristic sub-adapter into the base model
+    validate_merge_or_extract_adapter \
+        "http://${ip_address}:$finetuning_service_port/v1/finetune/merge_adapter" \
+        "merge heuristic sub-adapter" \
+        "test-comps-finetuning-server" \
+        "${FINTUNING_ID}" \
+        "{\"fine_tuning_job_id\": \"${FINTUNING_ID}\", \"adapter_version\": \"heuristic\"}"
+
+    # test extracting sub-adapter with custom configuration
+    validate_merge_or_extract_adapter \
+        "http://${ip_address}:$finetuning_service_port/v1/finetune/extract_sub_adapter" \
+        "extract custom sub-adapter" \
+        "test-comps-finetuning-server" \
+        "${FINTUNING_ID}" \
+        "{\"fine_tuning_job_id\": \"${FINTUNING_ID}\", \"adapter_version\": \"custom\", \"custom_config\": [8, 6, 4, 4, 8, 6, 8, 8, 8, 8, 4, 8]}"
+
+    # test merging the custom sub-adapter into the base model
+    validate_merge_or_extract_adapter \
+        "http://${ip_address}:$finetuning_service_port/v1/finetune/merge_adapter" \
+        "merge custom sub-adapter" \
+        "test-comps-finetuning-server" \
+        "${FINTUNING_ID}" \
+        "{\"fine_tuning_job_id\": \"${FINTUNING_ID}\", \"adapter_version\": \"custom\"}"
+
+}
+
 function stop_docker() {
     cid=$(docker ps -aq --filter "name=test-comps-finetuning-server*")
     if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
@@ -233,12 +353,16 @@ function stop_docker() {
 function main() {
 
     stop_docker
-
     build_docker_images
     start_service
-
     validate_microservice
 
+    # test sqft
+    stop_docker
+    build_sqft_docker_images
+    start_service
+    validate_sqft_microservice
+
     stop_docker
     echo y | docker system prune