diff --git a/examples/.config/model_params_pytorch.json b/examples/.config/model_params_pytorch.json
index b72462fdfa7..b34185327fb 100644
--- a/examples/.config/model_params_pytorch.json
+++ b/examples/.config/model_params_pytorch.json
@@ -471,6 +471,13 @@
       "main_script": "run_clm_no_trainer.py",
       "batch_size": 8
     },
+    "opt_125m_woq_gptq_debug_int4":{
+      "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/llm",
+      "dataset_location": "",
+      "input_model": "",
+      "main_script": "run_clm_no_trainer.py",
+      "batch_size": 8
+    },
     "opt_125m_woq_teq":{
       "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/llm",
       "dataset_location": "",
@@ -513,7 +520,14 @@
       "main_script": "run_clm_no_trainer.py",
       "batch_size": 1
     },
-    "gpt_j_woq_rtn":{
+    "gpt_j_woq_rtn_int4":{
+      "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/llm",
+      "dataset_location": "",
+      "input_model": "",
+      "main_script": "run_clm_no_trainer.py",
+      "batch_size": 1
+    },
+    "gpt_j_woq_gptq_debug_int4":{
       "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/llm",
       "dataset_location": "",
       "input_model": "",
@@ -527,6 +541,13 @@
       "main_script": "run_clm_no_trainer.py",
       "batch_size": 1
     },
+    "falcon_7b_woq_gptq_debug_int4":{
+      "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/llm",
+      "dataset_location": "",
+      "input_model": "",
+      "main_script": "run_clm_no_trainer.py",
+      "batch_size": 1
+    },
     "xlm-roberta-base_MRPC": {
       "model_src_dir": "nlp/huggingface_models/text-classification/quantization/ptq_static/fx",
       "dataset_location": "",
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/requirements.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/requirements.txt
index bf5db6ebb13..f409996c8b7 100644
--- a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/requirements.txt
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/requirements.txt
@@ -2,6 +2,7 @@ accelerate
 protobuf
 sentencepiece != 0.1.92
 datasets >= 1.1.3
+peft
 torch >= 1.10
 transformers
 pytest
@@ -9,5 +10,4 @@ wandb
 einops
 neural-compressor
 intel-extension-for-transformers
-git+https://github.com/EleutherAI/lm-evaluation-harness.git@83dbfbf6070324f3e5872f63e49d49ff7ef4c9b3
-git+https://github.com/huggingface/peft.git@6c44096c7b8d55a2ecf24be9bc68393467e1584a
+git+https://github.com/EleutherAI/lm-evaluation-harness.git@cc9778fbe4fa1a709be2abed9deb6180fd40e7e2
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_clm_no_trainer.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_clm_no_trainer.py
index 8b8e85f6e92..d2f3c0f5c60 100644
--- a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_clm_no_trainer.py
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_clm_no_trainer.py
@@ -52,7 +52,7 @@
                     help="calibration iters.")
 parser.add_argument("--tasks", nargs='+', default=["lambada_openai",
                                                    "hellaswag", "winogrande", "piqa", "wikitext"],
-                    type=str, help="tasks list for accuracy validation")
+                    type=str, help="tasks list for accuracy validation, text-generation and code-generation tasks are different.")
 parser.add_argument("--peft_model_id", type=str, default=None, help="model_name_or_path of peft model")
 # ============SmoothQuant configs==============
 parser.add_argument("--sq", action="store_true")
@@ -78,7 +78,40 @@
                                                                            this should align with your model config, \
                                                                            and your dataset builder args: args.pad_max_length')
 parser.add_argument('--gptq_debug', action='store_true', help='Whether to use debug model ')
-# =======================================
+# ==============code generation args===========
+parser.add_argument("--code_generation", action="store_true")
+parser.add_argument("--n_samples", default=200, type=int)
+parser.add_argument(
+    "--limit", default=None, type=int, help="Limit number of samples to eval"
+)
+parser.add_argument("--allow_code_execution", action="store_true")
+parser.add_argument("--prefix", default="")
+parser.add_argument("--generation_only", action="store_true")
+parser.add_argument("--postprocess", action="store_false")
+parser.add_argument("--save_references", action="store_true")
+parser.add_argument("--save_generations", action="store_true")
+parser.add_argument("--instruction_tokens", default=None)
+parser.add_argument("--save_generations_path", default="generations.json")
+parser.add_argument("--load_generations_path", default=None)
+parser.add_argument("--metric_output_path", default="evaluation_results.json")
+parser.add_argument("--max_length_generation", default=512, type=int)
+parser.add_argument("--temperature", default=0.8, type=float)
+parser.add_argument("--top_p", default=0.8, type=float)
+parser.add_argument("--top_k", default=0, type=int)
+parser.add_argument("--do_sample", action="store_true")
+parser.add_argument("--check_references", action="store_true")
+parser.add_argument("--max_memory_per_gpu", type=str, default=None)
+parser.add_argument(
+    "--modeltype",
+    default="causal",
+    help="AutoModel to use, it can be causal or seq2seq",
+)
+parser.add_argument(
+    "--limit_start",
+    type=int,
+    default=0,
+    help="Optional offset to start from when limiting the number of samples",
+)
 
 args = parser.parse_args()
 if args.ipex:
@@ -262,7 +295,7 @@ def calib_func(prepared_model):
         if args.gptq_debug:
             from neural_compressor.adaptor.torch_utils.weight_only import gptq_quantize
 
-            conf = {
+            gptq_conf = {
                 ".*": {
                     'wbits': args.woq_bits,  # 1-8 bits
                     'group_size': args.woq_group_size,  # -1 (per-channel)
@@ -272,20 +305,16 @@ def calib_func(prepared_model):
             }
             q_model_gptq_debug, gptq_config = gptq_quantize(
                 user_model,
-                weight_config=conf,
+                weight_config=gptq_conf,
                 dataloader=calib_dataloader,
                 nsamples=args.gptq_nsamples,
                 use_max_length=args.gptq_use_max_length,
-                pad_max_length=args.gptq_pad_max_length
+                pad_max_length=args.gptq_pad_max_length,
             )
-            from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
 
-            results = evaluate(
-                model="hf-causal",
-                model_args='pretrained=' + args.model + ',tokenizer=' + args.model + ',dtype=float32',
-                user_model=q_model_gptq_debug, tasks=["lambada_openai"],
-                batch_size=4
-            )
+            # save the fake quantized model
+            os.makedirs(args.output_dir, exist_ok=True)
+            torch.save(q_model_gptq_debug, os.path.join(args.output_dir, "gptq_best_model.pt"))
             exit(0)
 
     else:
@@ -317,7 +346,6 @@ def calib_func(prepared_model):
             eval_dataset = load_dataset('lambada', split='validation')
             evaluator = Evaluator(eval_dataset, tokenizer)
 
-
             def eval_func(model):
                 acc = evaluator.evaluate(model)
                 return acc
@@ -347,15 +375,29 @@ def eval_func(model):
 
 if args.accuracy:
     user_model.eval()
-    from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
+    if args.gptq_debug:
+        user_model = torch.load(os.path.join(args.output_dir, "gptq_best_model.pt"))
+    if args.code_generation:
+        from intel_extension_for_transformers.llm.evaluation.lm_code_eval import evaluate
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(args.model)
+        results = evaluate(
+            model=user_model,
+            tokenizer=tokenizer,
+            tasks=",".join(args.tasks),
+            batch_size=args.batch_size,
+            args=args,
+        )
+    else:
+        from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
+        results = evaluate(
+            model="hf-causal",
+            model_args='pretrained=' + args.model + ',tokenizer=' + args.model + ',dtype=float32',
+            user_model=user_model,
+            batch_size=args.batch_size,
+            tasks=args.tasks,
+        )
 
-    results = evaluate(
-        model="hf-causal",
-        model_args='pretrained=' + args.model + ',tokenizer=' + args.model + ',dtype=float32',
-        user_model=user_model,
-        batch_size=args.batch_size,
-        tasks=args.tasks,
-    )
     dumped = json.dumps(results, indent=2)
     if args.save_accuracy_path:
         with open(args.save_accuracy_path, "w") as f:
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_quant.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_quant.sh
index df2945f79b4..179cb3cec22 100644
--- a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_quant.sh
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/run_quant.sh
@@ -50,6 +50,10 @@ function run_tuning {
         model_name_or_path="facebook/opt-125m"
         approach="weight_only"
         extra_cmd=$extra_cmd" --woq_algo GPTQ"
+    elif [ "${topology}" = "opt_125m_woq_gptq_debug_int4" ]; then
+        model_name_or_path="facebook/opt-125m"
+        approach="weight_only"
+        extra_cmd=$extra_cmd" --woq_algo GPTQ --woq_bits 4 --woq_scheme asym --woq_group_size 128 --gptq_use_max_length --gptq_debug"
     elif [ "${topology}" = "opt_125m_woq_teq" ]; then
         model_name_or_path="facebook/opt-125m"
         approach="weight_only"
@@ -69,13 +73,21 @@ function run_tuning {
     elif [ "${topology}" = "gpt_j_ipex_sq" ]; then
         model_name_or_path="EleutherAI/gpt-j-6b"
         extra_cmd=$extra_cmd" --ipex --sq --alpha 1.0"
-    elif [ "${topology}" = "gpt_j_woq_rtn" ]; then
+    elif [ "${topology}" = "gpt_j_woq_rtn_int4" ]; then
         model_name_or_path="EleutherAI/gpt-j-6b"
         approach="weight_only"
         extra_cmd=$extra_cmd" --woq_algo RTN --woq_bits 4 --woq_group_size 128 --woq_scheme asym --woq_enable_mse_search"
+    elif [ "${topology}" = "gpt_j_woq_gptq_debug_int4" ]; then
+        model_name_or_path="EleutherAI/gpt-j-6b"
+        approach="weight_only"
+        extra_cmd=$extra_cmd" --woq_algo GPTQ --woq_bits 4 --woq_group_size 128 --woq_scheme asym --gptq_use_max_length --gptq_debug"
     elif [ "${topology}" = "falcon_7b_sq" ]; then
         model_name_or_path="tiiuae/falcon-7b-instruct"
         extra_cmd=$extra_cmd" --sq --alpha 0.5"
+    elif [ "${topology}" = "falcon_7b_woq_gptq_debug_int4" ]; then
+        model_name_or_path="tiiuae/falcon-7b-instruct"
+        approach="weight_only"
+        extra_cmd=$extra_cmd" --woq_algo GPTQ --woq_bits 4 --woq_group_size 128 --woq_scheme asym --gptq_use_max_length --gptq_debug"
     fi
 
     python -u run_clm_no_trainer.py \