diff --git a/natural_language_processing/text_generation/alpaca/run.py b/natural_language_processing/text_generation/alpaca/run.py
index 0e095813..6c234f43 100644
--- a/natural_language_processing/text_generation/alpaca/run.py
+++ b/natural_language_processing/text_generation/alpaca/run.py
@@ -22,11 +22,13 @@ def run_pytorch(model_path, num_runs, timeout, dataset_path, use_torch_fp16=Fals
     from utils.nlp.alpaca_instruct import AlpacaInstruct
     from utils.pytorch import PyTorchRunnerV2, apply_compile
     from utils.benchmark import run_model
-    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
     def run_single_pass(pytorch_runner, _dataset):
         inputs = encode(_dataset.get_input_string())
-        outputs = pytorch_runner.run(inputs=inputs.input_ids, max_new_tokens=100)
+        config = GenerationConfig()
+        config.max_new_tokens = 100
+        outputs = pytorch_runner.run(inputs=inputs.input_ids, generation_config=config)
         pytorch_runner.set_task_size(outputs.shape[1] - inputs.input_ids.shape[1])
         response = decode(outputs[:, inputs.input_ids.shape[1]:])
         _dataset.submit_prediction(response)
@@ -35,7 +37,7 @@ def run_single_pass(pytorch_runner, _dataset):
     if use_torch_fp16:
         model = model.half()
     model.eval()
-    model.greedy_search = apply_compile(model.greedy_search)
+    model.forward = apply_compile(model.forward)
 
     tokenizer = AutoTokenizer.from_pretrained(model_path)
     dataset = AlpacaInstruct(1, dataset_path=dataset_path)
diff --git a/natural_language_processing/text_generation/llama2/run.py b/natural_language_processing/text_generation/llama2/run.py
index 74d46d4e..182e1797 100644
--- a/natural_language_processing/text_generation/llama2/run.py
+++ b/natural_language_processing/text_generation/llama2/run.py
@@ -22,12 +22,14 @@ def run_pytorch(model_name, batch_size, num_runs, timeout, dataset_path, use_tor
     from utils.pytorch import PyTorchRunnerV2, apply_compile
     from utils.benchmark import run_model
     from utils.nlp.alpaca_instruct import AlpacaInstruct
-    from transformers import LlamaForCausalLM, AutoTokenizer
+    from transformers import LlamaForCausalLM, AutoTokenizer, GenerationConfig
 
     def run_single_pass(pytorch_runner, _dataset):
         input_tensor = tokenizer.encode(_dataset.get_input_string(), return_tensors="pt")
         input_tensor = torch.cat([input_tensor for _ in range(batch_size)], 0)
-        output = pytorch_runner.run(inputs=input_tensor, max_length=400)
+        config = GenerationConfig()
+        config.max_length = 400
+        output = pytorch_runner.run(inputs=input_tensor, generation_config=config)
         pytorch_runner.set_task_size(sum([len(output[i]) - len(input_tensor[i]) for i in range(batch_size)]))
         for i in range(batch_size):
             _dataset.submit_prediction(tokenizer.decode(output[i][len(input_tensor[i]):], skip_special_tokens=True))
@@ -41,7 +43,7 @@ def run_single_pass(pytorch_runner, _dataset):
     model.eval()
     if use_torch_fp16:
         model = model.half()
-    model.generate = apply_compile(model.generate)
+    model.forward = apply_compile(model.forward)
 
     tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
     tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
diff --git a/setup_deb.sh b/setup_deb.sh
index 8342a83f..863d569e 100644
--- a/setup_deb.sh
+++ b/setup_deb.sh
@@ -93,7 +93,7 @@ pip3 install --no-deps --upgrade \
    cchardet==2.1.7
 
 pip3 install --no-build-isolation --upgrade \
-   git+https://github.com/AmpereComputingAI/transformers.git@ampere/v4.36
+   git+https://github.com/AmpereComputingAI/transformers.git@ampere/v4.40
 
 # dependencies of dependencies
 pip3 install --no-deps --upgrade \
@@ -116,7 +116,7 @@ pip3 install --no-deps --upgrade \
    scikit-image==0.19.2 \
    scikit-learn==1.0.2 \
    threadpoolctl==3.1.0 \
-   tokenizers==0.14.1 \
+   tokenizers==0.19.1 \
    tabulate==0.9.0 \
    regex==2022.3.15 \
    portalocker==2.6.0 \
diff --git a/speech_recognition/whisper/run_hf.py b/speech_recognition/whisper/run_hf.py
index 9095ff2e..2950dc23 100644
--- a/speech_recognition/whisper/run_hf.py
+++ b/speech_recognition/whisper/run_hf.py
@@ -38,7 +38,8 @@ def run_pytorch_fp32(model_name, batch_size, num_runs, timeout, **kwargs):
         librispeech = LibriSpeech()  # reset
         model = model.generate
     else:
-        model = apply_compile(model.generate)
+        model.forward = apply_compile(model.forward)
+        model.model.encoder = apply_compile(model.model.encoder)
 
     def single_pass_pytorch(_runner, _librispeech):
         waveform = [_librispeech.get_input_array() for _ in range(batch_size)]
@@ -49,7 +50,7 @@ def single_pass_pytorch(_runner, _librispeech):
         for i in range(batch_size):
             _librispeech.submit_transcription(decoded_output[i].lstrip().replace(",", "").replace(".", "").upper())
 
-    runner = PyTorchRunnerV2(model, throughput_only=True)
+    runner = PyTorchRunnerV2(model.generate, throughput_only=True)
     print_warning_message("Sampling rate Whisper operates at is 16,000 Hz, therefore throughput values below can be "
                           "divided by 16,000 to derive 'seconds of processed audio per second'")
     return run_model(single_pass_pytorch, runner, librispeech, batch_size, num_runs, timeout)