openvino/docs/notebooks/240-dolly-2-instruction-following-with-output.rst

Instruction following using Databricks Dolly 2.0 and OpenVINO
=============================================================

The instruction following is one of the cornerstones of the current
generation of large language models(LLMs). Reinforcement learning with
human preferences (`RLHF <https://arxiv.org/abs/1909.08593>`__) and
techniques such as `InstructGPT <https://arxiv.org/abs/2203.02155>`__
has been the core foundation of breakthroughs such as ChatGPT and GPT-4.
However, these powerful models remain hidden behind APIs and we know
very little about their underlying architecture. Instruction-following
models are capable of generating text in response to prompts and are
often used for tasks like writing assistance, chatbots, and content
generation. Many users now interact with these models regularly and even
use them for work but the majority of such models remain closed-source
and require massive amounts of computational resources to experiment
with.

`Dolly
2.0 <https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm>`__
is the first open-source, instruction-following LLM fine-tuned by
Databricks on a transparent and freely available dataset that is also
open-sourced to use for commercial purposes. That means Dolly 2.0 is
available for commercial applications without the need to pay for API
access or share data with third parties. Dolly 2.0 exhibits similar
characteristics so ChatGPT despite being much smaller.

In this tutorial, we consider how to run an instruction-following text
generation pipeline using Dolly 2.0 and OpenVINO. We will use a
pre-trained model from the `Hugging Face
Transformers <https://huggingface.co/docs/transformers/index>`__
library. To simplify the user experience, the `Hugging Face Optimum
Intel <https://huggingface.co/docs/optimum/intel/index>`__ library is
used to convert the models to OpenVINO™ IR format.

The tutorial consists of the following steps:

-  Install prerequisites
-  Download and convert the model from a public source using the
   `OpenVINO integration with Hugging Face
   Optimum <https://huggingface.co/blog/openvino>`__.
-  Compress model weights to INT8 with `OpenVINO
   NNCF <https://github.com/openvinotoolkit/nncf>`__
-  Create an instruction-following inference pipeline
-  Run instruction-following pipeline

About Dolly 2.0
---------------

Dolly 2.0 is an instruction-following large language model trained on
the Databricks machine-learning platform that is licensed for commercial
use. It is based on `Pythia <https://github.com/EleutherAI/pythia>`__
and is trained on ~15k instruction/response fine-tuning records
generated by Databricks employees in various capability domains,
including brainstorming, classification, closed QA, generation,
information extraction, open QA, and summarization. Dolly 2.0 works by
processing natural language instructions and generating responses that
follow the given instructions. It can be used for a wide range of
applications, including closed question-answering, summarization, and
generation.

The model training process was inspired by
`InstructGPT <https://arxiv.org/abs/2203.02155>`__. To train InstructGPT
models, the core technique is reinforcement learning from human feedback
(RLHF), This technique uses human preferences as a reward signal to
fine-tune models, which is important as the safety and alignment
problems required to be solved are complex and subjective, and aren’t
fully captured by simple automatic metrics. More details about the
InstructGPT approach can be found in OpenAI `blog
post <https://openai.com/research/instruction-following>`__ The
breakthrough discovered with InstructGPT is that language models don’t
need larger and larger training sets. By using human-evaluated
question-and-answer training, authors were able to train a better
language model using one hundred times fewer parameters than the
previous model. Databricks used a similar approach to create a prompt
and response dataset called they call
`databricks-dolly-15k <https://huggingface.co/datasets/databricks/databricks-dolly-15k>`__,
a corpus of more than 15,000 records generated by thousands of
Databricks employees to enable large language models to exhibit the
magical interactivity of InstructGPT. More details about the model and
dataset can be found in `Databricks blog
post <https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm>`__
and `repo <https://github.com/databrickslabs/dolly>`__

**Table of contents:**


-  `Prerequisites <#prerequisites>`__

   -  `Select inference device <#select-inference-device>`__

-  `Download and Convert Model <#download-and-convert-model>`__

   -  `NNCF model weights
      compression <#nncf-model-weights-compression>`__

-  `Create an instruction-following inference
   pipeline <#create-an-instruction-following-inference-pipeline>`__

   -  `Setup imports <#setup-imports>`__
   -  `Prepare template for user
      prompt <#prepare-template-for-user-prompt>`__
   -  `Helpers for output parsing <#helpers-for-output-parsing>`__
   -  `Main generation function <#main-generation-function>`__
   -  `Helpers for application <#helpers-for-application>`__

-  `Run instruction-following
   pipeline <#run-instruction-following-pipeline>`__

Prerequisites
-------------



First, we should install the `Hugging Face
Optimum <https://huggingface.co/docs/optimum/installation>`__ library
accelerated by OpenVINO integration. The Hugging Face Optimum Intel API
is a high-level API that enables us to convert and quantize models from
the Hugging Face Transformers library to the OpenVINO™ IR format. For
more details, refer to the `Hugging Face Optimum Intel
documentation <https://huggingface.co/docs/optimum/intel/inference>`__.

.. code:: ipython3

    %pip install -q "diffusers>=0.16.1" "transformers>=4.33.0" "openvino>=2023.2.0" "nncf>=2.6.0" datasets onnx gradio --extra-index-url https://download.pytorch.org/whl/cpu
    %pip install -q --upgrade "git+https://github.com/huggingface/optimum-intel.git" 

Select inference device
~~~~~~~~~~~~~~~~~~~~~~~



select device from dropdown list for running inference using OpenVINO

.. code:: ipython3

    import ipywidgets as widgets
    import openvino as ov
    
    core = ov.Core()
    
    device = widgets.Dropdown(
        options=core.available_devices + ["AUTO"],
        value='CPU',
        description='Device:',
        disabled=False,
    )
    
    device




.. parsed-literal::

    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')



Download and Convert Model
--------------------------



Optimum Intel can be used to load optimized models from the `Hugging
Face Hub <https://huggingface.co/docs/optimum/intel/hf.co/models>`__ and
create pipelines to run an inference with OpenVINO Runtime using Hugging
Face APIs. The Optimum Inference models are API compatible with Hugging
Face Transformers models. This means we just need to replace
``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
class.

Below is an example of the Dolly model

.. code:: diff

   -from transformers import AutoModelForCausalLM
   +from optimum.intel.openvino import OVModelForCausalLM
   from transformers import AutoTokenizer, pipeline

   model_id = "databricks/dolly-v2-3b"
   -model = AutoModelForCausalLM.from_pretrained(model_id)
   +model = OVModelForCausalLM.from_pretrained(model_id, from_transformers=True)

Model class initialization starts with calling ``from_pretrained``
method. When downloading and converting Transformers model, the
parameter ``export=True`` should be added. For models where size more We
can save the converted model for the next usage with the
``save_pretrained`` method. Tokenizer class and pipelines API are
compatible with Optimum models.

.. code:: ipython3

    from pathlib import Path
    from transformers import AutoTokenizer
    from optimum.intel.openvino import OVModelForCausalLM
    
    model_id = "databricks/dolly-v2-3b"
    model_path = Path("dolly-v2-3b")
    
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    
    current_device = device.value
    
    ov_config = {'PERFORMANCE_HINT': 'LATENCY', 'NUM_STREAMS': '1', "CACHE_DIR": ""}
    
    if model_path.exists():
        ov_model = OVModelForCausalLM.from_pretrained(model_path, device=current_device, ov_config=ov_config)
    else:
        ov_model = OVModelForCausalLM.from_pretrained(model_id, device=current_device, export=True, ov_config=ov_config, load_in_8bit=False)
        ov_model.half()
        ov_model.save_pretrained(model_path)


.. parsed-literal::

    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino


.. parsed-literal::

    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
    2023-11-17 13:10:43.359093: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
    2023-11-17 13:10:43.398436: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
    2023-11-17 13:10:44.026743: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
    Compiling the model to CPU ...


NNCF model weights compression
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



NNCF `Weights Compression
algorithm <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/CompressWeights.md>`__
compresses weights of a model to ``INT8``. This is an alternative to
`Quantization
algorithm <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/post_training/Quantization.md>`__
that compresses both weights and activations. Weight compression is
effective in optimizing footprint and performance of large models where
the size of weights is significantly larger than the size of
activations, for example, in Large Language Models (LLMs) such as Dolly
2.0. Additionally, Weight Compression usually leads to almost no
accuracy drop.

.. code:: ipython3

    to_compress = widgets.Checkbox(
        value=True,
        description='INT8 Compression',
        disabled=False,
    )
    print("Click on checkbox for enabling / disabling weights compression")
    to_compress


.. parsed-literal::

    Click on checkbox for enabling / disabling weights compression




.. parsed-literal::

    Checkbox(value=True, description='INT8 Compression')



.. code:: ipython3

    import gc
    from optimum.intel import OVQuantizer
    
    compressed_model_path = Path(f'{model_path}_compressed')
    
    def calculate_compression_rate(model_path_ov, model_path_ov_compressed):
        model_size_original = model_path_ov.with_suffix(".bin").stat().st_size / 2 ** 20
        model_size_compressed = model_path_ov_compressed.with_suffix(".bin").stat().st_size / 2 ** 20
        print(f"* Original IR model size: {model_size_original:.2f} MB")
        print(f"* Compressed IR model size: {model_size_compressed:.2f} MB")
        print(f"* Model compression rate: {model_size_original / model_size_compressed:.3f}")
    
    if to_compress.value:
        if not compressed_model_path.exists():
            quantizer = OVQuantizer.from_pretrained(ov_model)
            quantizer.quantize(save_directory=compressed_model_path, weights_only=True)
            del quantizer
            gc.collect()
        
        calculate_compression_rate(model_path / 'openvino_model.xml', compressed_model_path / 'openvino_model.xml')
        ov_model = OVModelForCausalLM.from_pretrained(compressed_model_path, device=current_device, ov_config=ov_config)


.. parsed-literal::

    * Original IR model size: 5297.21 MB
    * Compressed IR model size: 2657.89 MB
    * Model compression rate: 1.993


.. parsed-literal::

    Compiling the model to CPU ...


Create an instruction-following inference pipeline
--------------------------------------------------



The ``run_generation`` function accepts user-provided text input,
tokenizes it, and runs the generation process. Text generation is an
iterative process, where each next token depends on previously generated
until a maximum number of tokens or stop generation condition is not
reached. To obtain intermediate generation results without waiting until
when generation is finished, we will use
`TextIteratorStreamer <https://huggingface.co/docs/transformers/main/en/internal/generation_utils#transformers.TextIteratorStreamer>`__,
provided as part of HuggingFace `Streaming
API <https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming>`__.

The diagram below illustrates how the instruction-following pipeline
works

.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/e881f4a4-fcc8-427a-afe1-7dd80aebd66e
   :alt: generation pipeline)

   generation pipeline)

As can be seen, on the first iteration, the user provided instructions
converted to token ids using a tokenizer, then prepared input provided
to the model. The model generates probabilities for all tokens in logits
format The way the next token will be selected over predicted
probabilities is driven by the selected decoding methodology. You can
find more information about the most popular decoding methods in this
`blog <https://huggingface.co/blog/how-to-generate>`__.

There are several parameters that can control text generation quality:

-  | ``Temperature`` is a parameter used to control the level of
     creativity in AI-generated text. By adjusting the ``temperature``,
     you can influence the AI model’s probability distribution, making
     the text more focused or diverse.
   | Consider the following example: The AI model has to complete the
     sentence “The cat is \____.” with the following token
     probabilities:

   | playing: 0.5
   | sleeping: 0.25
   | eating: 0.15
   | driving: 0.05
   | flying: 0.05

   -  **Low temperature** (e.g., 0.2): The AI model becomes more focused
      and deterministic, choosing tokens with the highest probability,
      such as “playing.”
   -  **Medium temperature** (e.g., 1.0): The AI model maintains a
      balance between creativity and focus, selecting tokens based on
      their probabilities without significant bias, such as “playing,”
      “sleeping,” or “eating.”
   -  **High temperature** (e.g., 2.0): The AI model becomes more
      adventurous, increasing the chances of selecting less likely
      tokens, such as “driving” and “flying.”

-  ``Top-p``, also known as nucleus sampling, is a parameter used to
   control the range of tokens considered by the AI model based on their
   cumulative probability. By adjusting the ``top-p`` value, you can
   influence the AI model’s token selection, making it more focused or
   diverse. Using the same example with the cat, consider the following
   top_p settings:

   -  **Low top_p** (e.g., 0.5): The AI model considers only tokens with
      the highest cumulative probability, such as “playing.”
   -  **Medium top_p** (e.g., 0.8): The AI model considers tokens with a
      higher cumulative probability, such as “playing,” “sleeping,” and
      “eating.”
   -  **High top_p** (e.g., 1.0): The AI model considers all tokens,
      including those with lower probabilities, such as “driving” and
      “flying.”

-  ``Top-k`` is another popular sampling strategy. In comparison with
   Top-P, which chooses from the smallest possible set of words whose
   cumulative probability exceeds the probability P, in Top-K sampling K
   most likely next words are filtered and the probability mass is
   redistributed among only those K next words. In our example with cat,
   if k=3, then only “playing”, “sleeping” and “eating” will be taken
   into account as possible next word.

To optimize the generation process and use memory more efficiently, the
``use_cache=True`` option is enabled. Since the output side is
auto-regressive, an output token hidden state remains the same once
computed for every further generation step. Therefore, recomputing it
every time you want to generate a new token seems wasteful. With the
cache, the model saves the hidden state once it has been computed. The
model only computes the one for the most recently generated output token
at each time step, re-using the saved ones for hidden tokens. This
reduces the generation complexity from O(n^3) to O(n^2) for a
transformer model. More details about how it works can be found in this
`article <https://scale.com/blog/pytorch-improvements#Text%20Translation>`__.
With this option, the model gets the previous step’s hidden states
(cached attention keys and values) as input and additionally provides
hidden states for the current step as output. It means for all next
iterations, it is enough to provide only a new token obtained from the
previous step and cached key values to get the next token prediction.

The generation cycle repeats until the end of the sequence token is
reached or it also can be interrupted when maximum tokens will be
generated. As already mentioned before, we can enable printing current
generated tokens without waiting until when the whole generation is
finished using Streaming API, it adds a new token to the output queue
and then prints them when they are ready.

Setup imports
~~~~~~~~~~~~~



.. code:: ipython3

    from threading import Thread
    from time import perf_counter
    from typing import List
    import gradio as gr
    from transformers import AutoTokenizer, TextIteratorStreamer
    import numpy as np

Prepare template for user prompt
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



For effective generation, model expects to have input in specific
format. The code below prepare template for passing user instruction
into model with providing additional context.

.. code:: ipython3

    INSTRUCTION_KEY = "### Instruction:"
    RESPONSE_KEY = "### Response:"
    END_KEY = "### End"
    INTRO_BLURB = (
        "Below is an instruction that describes a task. Write a response that appropriately completes the request."
    )
    
    # This is the prompt that is used for generating responses using an already trained model.  It ends with the response
    # key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
    PROMPT_FOR_GENERATION_FORMAT = """{intro}
    
    {instruction_key}
    {instruction}
    
    {response_key}
    """.format(
        intro=INTRO_BLURB,
        instruction_key=INSTRUCTION_KEY,
        instruction="{instruction}",
        response_key=RESPONSE_KEY,
    )

Helpers for output parsing
~~~~~~~~~~~~~~~~~~~~~~~~~~



Model was retrained to finish generation using special token ``### End``
the code below find its id for using it as generation stop-criteria.

.. code:: ipython3

    def get_special_token_id(tokenizer: AutoTokenizer, key: str) -> int:
        """
        Gets the token ID for a given string that has been added to the tokenizer as a special token.
    
        When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
        treated specially and converted to a single, new token.  This retrieves the token ID each of these keys map to.
    
        Args:
            tokenizer (PreTrainedTokenizer): the tokenizer
            key (str): the key to convert to a single token
    
        Raises:
            RuntimeError: if more than one ID was generated
    
        Returns:
            int: the token ID for the given key
        """
        token_ids = tokenizer.encode(key)
        if len(token_ids) > 1:
            raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
        return token_ids[0]
    
    tokenizer_response_key = next((token for token in tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None)
    
    end_key_token_id = None
    if tokenizer_response_key:
        try:
            end_key_token_id = get_special_token_id(tokenizer, END_KEY)
            # Ensure generation stops once it generates "### End"
        except ValueError:
            pass

Main generation function
~~~~~~~~~~~~~~~~~~~~~~~~



As it was discussed above, ``run_generation`` function is the entry
point for starting generation. It gets provided input instruction as
parameter and returns model response.

.. code:: ipython3

    def run_generation(user_text:str, top_p:float, temperature:float, top_k:int, max_new_tokens:int, perf_text:str):
        """
        Text generation function
        
        Parameters:
          user_text (str): User-provided instruction for a generation.
          top_p (float):  Nucleus sampling. If set to < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for a generation.
          temperature (float): The value used to module the logits distribution.
          top_k (int): The number of highest probability vocabulary tokens to keep for top-k-filtering.
          max_new_tokens (int): Maximum length of generated sequence.
          perf_text (str): Content of text field for printing performance results.
        Returns:
          model_output (str) - model-generated text
          perf_text (str) - updated perf text filed content
        """
        
        # Prepare input prompt according to model expected template
        prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=user_text)
        
        # Tokenize the user text.
        model_inputs = tokenizer(prompt_text, return_tensors="pt")
    
        # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
        # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
        generate_kwargs = dict(
            model_inputs,
            streamer=streamer,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            top_p=top_p,
            temperature=float(temperature),
            top_k=top_k,
            eos_token_id=end_key_token_id
        )
        t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
        t.start()
    
        # Pull the generated text from the streamer, and update the model output.
        model_output = ""
        per_token_time = []
        num_tokens = 0
        start = perf_counter()
        for new_text in streamer:
            current_time = perf_counter() - start
            model_output += new_text
            perf_text, num_tokens = estimate_latency(current_time, perf_text, new_text, per_token_time, num_tokens)
            yield model_output, perf_text
            start = perf_counter()
        return model_output, perf_text

Helpers for application
~~~~~~~~~~~~~~~~~~~~~~~



For making interactive user interface we will use Gradio library. The
code bellow provides useful functions used for communication with UI
elements.

.. code:: ipython3

    def estimate_latency(current_time:float, current_perf_text:str, new_gen_text:str, per_token_time:List[float], num_tokens:int):
        """
        Helper function for performance estimation
        
        Parameters:
          current_time (float): This step time in seconds.
          current_perf_text (str): Current content of performance UI field.
          new_gen_text (str): New generated text.
          per_token_time (List[float]): history of performance from previous steps.
          num_tokens (int): Total number of generated tokens.
          
        Returns:
          update for performance text field
          update for a total number of tokens
        """
        num_current_toks = len(tokenizer.encode(new_gen_text))
        num_tokens += num_current_toks
        per_token_time.append(num_current_toks / current_time)
        if len(per_token_time) > 10 and len(per_token_time) % 4 == 0:
            current_bucket = per_token_time[:-10]
            return f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}", num_tokens
        return current_perf_text, num_tokens
    
    def reset_textbox(instruction:str, response:str, perf:str):
        """
        Helper function for resetting content of all text fields
        
        Parameters:
          instruction (str): Content of user instruction field.
          response (str): Content of model response field.
          perf (str): Content of performance info filed
        
        Returns:
          empty string for each placeholder
        """
        return "", "", ""
    
    
    def select_device(device_str:str, current_text:str = "", progress:gr.Progress = gr.Progress()):
        """
        Helper function for uploading model on the device.
        
        Parameters:
          device_str (str): Device name.
          current_text (str): Current content of user instruction field (used only for backup purposes, temporally replacing it on the progress bar during model loading).
          progress (gr.Progress): gradio progress tracker
        Returns:
          current_text
        """
        if device_str != ov_model._device:
            ov_model.request = None
            ov_model._device = device_str
            
            for i in progress.tqdm(range(1), desc=f"Model loading on {device_str}"):
                ov_model.compile()
        return current_text

Run instruction-following pipeline
----------------------------------



Now, we are ready to explore model capabilities. This demo provides a
simple interface that allows communication with a model using text
instruction. Type your instruction into the ``User instruction`` field
or select one from predefined examples and click on the ``Submit``
button to start generation. Additionally, you can modify advanced
generation parameters:

-  ``Device`` - allows switching inference device. Please note, every
   time when new device is selected, model will be recompiled and this
   takes some time.
-  ``Max New Tokens`` - maximum size of generated text.
-  ``Top-p (nucleus sampling)`` - if set to < 1, only the smallest set
   of most probable tokens with probabilities that add up to top_p or
   higher are kept for a generation.
-  ``Top-k`` - the number of highest probability vocabulary tokens to
   keep for top-k-filtering.
-  ``Temperature`` - the value used to module the logits distribution.

.. code:: ipython3

    available_devices = ov.Core().available_devices + ["AUTO"]
    
    examples = [
        "Give me recipe for pizza with pineapple",
        "Write me a tweet about new OpenVINO release",
        "Explain difference between CPU and GPU",
        "Give five ideas for great weekend with family",
        "Do Androids dream of Electric sheep?",
        "Who is Dolly?",
        "Please give me advice how to write resume?",
        "Name 3 advantages to be a cat",
        "Write instructions on how to become a good AI engineer",
        "Write a love letter to my best friend",
    ]
    
    with gr.Blocks() as demo:
        gr.Markdown(
            "# Instruction following using Databricks Dolly 2.0 and OpenVINO.\n"
            "Provide insturction which describes a task below or select among predefined examples and model writes response that performs requested task."
        )
    
        with gr.Row():
            with gr.Column(scale=4):
                user_text = gr.Textbox(
                    placeholder="Write an email about an alpaca that likes flan",
                    label="User instruction"
                )
                model_output = gr.Textbox(label="Model response", interactive=False)
                performance = gr.Textbox(label="Performance", lines=1, interactive=False)
                with gr.Column(scale=1):
                    button_clear = gr.Button(value="Clear")
                    button_submit = gr.Button(value="Submit")
                gr.Examples(examples, user_text)
            with gr.Column(scale=1):
                device = gr.Dropdown(choices=available_devices, value=current_device, label="Device")
                max_new_tokens = gr.Slider(
                    minimum=1, maximum=1000, value=256, step=1, interactive=True, label="Max New Tokens",
                )
                top_p = gr.Slider(
                    minimum=0.05, maximum=1.0, value=0.92, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
                )
                top_k = gr.Slider(
                    minimum=0, maximum=50, value=0, step=1, interactive=True, label="Top-k",
                )
                temperature = gr.Slider(
                    minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature",
                )
    
        user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
        button_submit.click(select_device, [device, user_text], [user_text])
        button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens, performance], [model_output, performance])
        button_clear.click(reset_textbox, [user_text, model_output, performance], [user_text, model_output, performance])
        device.change(select_device, [device, user_text], [user_text])
    
    if __name__ == "__main__":
        try:
            demo.queue().launch(debug=False, height=800)
        except Exception:
            demo.queue().launch(debug=False, share=True, height=800)
    
    # If you are launching remotely, specify server_name and server_port
    # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
    # To learn more please refer to the Gradio docs: https://gradio.app/docs/