LLM Instruction-following pipeline with OpenVINO

This Jupyter notebook can be launched after a local installation only.

LLM stands for “Large Language Model,” which refers to a type of artificial intelligence model that is designed to understand and generate human-like text based on the input it receives. LLMs are trained on large datasets of text to learn patterns, grammar, and semantic relationships, allowing them to generate coherent and contextually relevant responses. One core capability of Large Language Models (LLMs) is to follow natural language instructions. 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.

In this tutorial, we consider how to run an instruction-following text generation pipeline using popular LLMs and OpenVINO. We will use pre-trained models from the Hugging Face Transformers library. To simplify the user experience, the Hugging Face Optimum Intel library converts 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.

• Compress model weights to INT8 and INT4 with OpenVINO NNCF

• Create an instruction-following inference pipeline

• Run instruction-following pipeline

Table of contents:

• Prerequisites

• Select model for inference

• Instantiate Model using Optimum Intel

• Compress model weights

  • Weights Compression using Optimum Intel

  • Weights Compression using NNCF

• Select device for inference and model variant

• Create an instruction-following inference pipeline

  • Setup imports

  • Prepare template for user prompt

  • Main generation function

  • Helpers for application

• Run instruction-following pipeline

Prerequisites

%pip uninstall -q -y openvino openvino-dev openvino-nightly optimum optimum-intel
%pip install -q openvino-nightly "nncf>=2.7" "transformers>=4.36.0" onnx "optimum>=1.16.1" "accelerate" "datasets" gradio "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu

Select model for inference

The tutorial supports different models, you can select one from the provided options to compare the quality of open source LLM solutions.

NOTE: conversion of some models can require additional actions from user side and at least 64GB RAM for conversion.

The available options are:

• tiny-llama-1b-chat - This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens with the adoption of the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. More details about model can be found in model card

• phi-2 - Phi-2 is a Transformer with 2.7 billion parameters. It was trained using the same data sources as Phi-1.5, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters. More details about model can be found in model card.

• dolly-v2-3b - 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 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. More details about model can be found in model card.

• red-pajama-3b-instruct - A 2.8B parameter pre-trained language model based on GPT-NEOX architecture. The model was fine-tuned for few-shot applications on the data of GPT-JT, with exclusion of tasks that overlap with the HELM core scenarios.More details about model can be found in model card.

• mistral-7b - The Mistral-7B-v0.2 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. You can find more details about model in the model card, paper and release blog post.

from config import SUPPORTED_LLM_MODELS
import ipywidgets as widgets

model_ids = list(SUPPORTED_LLM_MODELS)

model_id = widgets.Dropdown(
    options=model_ids,
    value=model_ids[1],
    description="Model:",
    disabled=False,
)

model_id

Dropdown(description='Model:', index=1, options=('tiny-llama-1b', 'phi-2', 'dolly-v2-3b', 'red-pajama-instruct…

model_configuration = SUPPORTED_LLM_MODELS[model_id.value]
print(f"Selected model {model_id.value}")

Selected model phi-2

Instantiate Model using Optimum Intel

Optimum Intel can be used to load optimized models from the Hugging Face Hub 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 RedPajama model

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

model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-model = AutoModelForCausalLM.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id, export=True)

Model class initialization starts with calling from_pretrained method. When downloading and converting the Transformers model, the parameter export=True should be added. 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.

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. 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.

Compress model weights

The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). Compared to INT8 compression, INT4 compression improves performance even more but introduces a minor drop in prediction quality.

Weights Compression using Optimum Intel

To enable weight compression via NNCF for models supported by Optimum Intel OVQuantizer class should be used for OVModelForCausalLM model. OVQuantizer.quantize(save_directory=save_dir, weights_only=True) enables weights compression. An example of this approach usage you can find in llm-chatbot notebook

Weights Compression using NNCF

You also can perform weights compression for OpenVINO models using NNCF directly. nncf.compress_weights function accepts the OpenVINO model instance and compresses its weights for Linear and Embedding layers. We will consider this variant in this notebook for both int4 and int8 compression.

NOTE: This tutorial involves conversion model for FP16 and INT4/INT8 weights compression scenarios. It may be memory and time-consuming in the first run. You can manually control the compression precision below. NOTE: There may be no speedup for INT4/INT8 compressed models on dGPU

from IPython.display import display

prepare_int4_model = widgets.Checkbox(
    value=True,
    description="Prepare INT4 model",
    disabled=False,
)
prepare_int8_model = widgets.Checkbox(
    value=False,
    description="Prepare INT8 model",
    disabled=False,
)
prepare_fp16_model = widgets.Checkbox(
    value=False,
    description="Prepare FP16 model",
    disabled=False,
)

display(prepare_int4_model)
display(prepare_int8_model)
display(prepare_fp16_model)

Checkbox(value=True, description='Prepare INT4 model')

Checkbox(value=False, description='Prepare INT8 model')

Checkbox(value=False, description='Prepare FP16 model')

from pathlib import Path
import shutil
import logging
import openvino as ov
import nncf
from optimum.intel.openvino import OVModelForCausalLM
from optimum.utils import NormalizedTextConfig, NormalizedConfigManager
import gc

NormalizedConfigManager._conf['phi'] = NormalizedTextConfig

nncf.set_log_level(logging.ERROR)

pt_model_id = model_configuration["model_id"]
fp16_model_dir = Path(model_id.value) / "FP16"
int8_model_dir = Path(model_id.value) / "INT8_compressed_weights"
int4_model_dir = Path(model_id.value) / "INT4_compressed_weights"

core = ov.Core()

def convert_to_fp16():
    if (fp16_model_dir / "openvino_model.xml").exists():
        return
    ov_model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False, load_in_8bit=False)
    ov_model.half()
    ov_model.save_pretrained(fp16_model_dir)
    del ov_model
    gc.collect()


def convert_to_int8():
    if (int8_model_dir / "openvino_model.xml").exists():
        return
    int8_model_dir.mkdir(parents=True, exist_ok=True)
    if fp16_model_dir.exists():
        model = core.read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int8_model_dir / "config.json")
    else:
        ov_model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False, load_in_8bit=False)
        ov_model.half()
        ov_model.config.save_pretrained(int8_model_dir)
        model = ov_model._original_model
        del ov_model
        gc.collect()

    compressed_model = nncf.compress_weights(model)
    ov.save_model(compressed_model, int8_model_dir / "openvino_model.xml")
    del ov_model
    del compressed_model
    gc.collect()


def convert_to_int4():
    compression_configs = {
        "mistral-7b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        'red-pajama-3b-instruct': {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "group_size": 128,
            "ratio": 0.5,
        },
        "dolly-v2-3b": {"mode": nncf.CompressWeightsMode.INT4_ASYM, "group_size": 32, "ratio": 0.5},
        "default": {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "group_size": 128,
            "ratio": 0.8,
        },
    }

    model_compression_params = compression_configs.get(
        model_id.value, compression_configs["default"]
    )
    if (int4_model_dir / "openvino_model.xml").exists():
        return
    int4_model_dir.mkdir(parents=True, exist_ok=True)
    if not fp16_model_dir.exists():
        model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False, load_in_8bit=False).half()
        model.config.save_pretrained(int4_model_dir)
        ov_model = model._original_model
        del model
        gc.collect()
    else:
        ov_model = core.read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")
    compressed_model = nncf.compress_weights(ov_model, **model_compression_params)
    ov.save_model(compressed_model, int4_model_dir / "openvino_model.xml")
    del ov_model
    del compressed_model
    gc.collect()


if prepare_fp16_model.value:
    convert_to_fp16()
if prepare_int8_model.value:
    convert_to_int8()
if prepare_int4_model.value:
    convert_to_int4()

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

/home/ea/work/genai_env/lib/python3.8/site-packages/torch/cuda/__init__.py:138: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11080). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'

Let’s compare model size for different compression types

fp16_weights = fp16_model_dir / "openvino_model.bin"
int8_weights = int8_model_dir / "openvino_model.bin"
int4_weights = int4_model_dir / "openvino_model.bin"

if fp16_weights.exists():
    print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
    if compressed_weights.exists():
        print(
            f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB"
        )
    if compressed_weights.exists() and fp16_weights.exists():
        print(
            f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}"
        )

Size of model with INT4 compressed weights is 1734.02 MB

Select device for inference and model variant

NOTE: There may be no speedup for INT4/INT8 compressed models on dGPU.

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

device

Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')

available_models = []
if int4_model_dir.exists():
    available_models.append("INT4")
if int8_model_dir.exists():
    available_models.append("INT8")
if fp16_model_dir.exists():
    available_models.append("FP16")

model_to_run = widgets.Dropdown(
    options=available_models,
    value=available_models[0],
    description="Model to run:",
    disabled=False,
)

model_to_run

Dropdown(description='Model to run:', options=('INT4',), value='INT4')

from transformers import AutoTokenizer

if model_to_run.value == "INT4":
    model_dir = int4_model_dir
elif model_to_run.value == "INT8":
    model_dir = int8_model_dir
else:
    model_dir = fp16_model_dir
print(f"Loading model from {model_dir}")

model_name = model_configuration["model_id"]
ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}

tok = AutoTokenizer.from_pretrained(model_name)

ov_model = OVModelForCausalLM.from_pretrained(
    model_dir,
    device=device.value,
    ov_config=ov_config,
)

Loading model from phi-2/INT4_compressed_weights

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
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, provided as part of HuggingFace Streaming API.

The diagram below illustrates how the instruction-following pipeline works

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.

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. 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

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.

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})


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.

    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]

response_key = model_configuration.get("response_key")
tokenizer_response_key = None

if response_key is not None:
    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 = model_configuration.get("end_key")
        if end_key:
            end_key_token_id = get_special_token_id(tokenizer, end_key)
        # Ensure generation stops once it generates "### End"
    except ValueError:
        pass

prompt_template = model_configuration.get("prompt_template", "{instruction}")
end_key_token_id = end_key_token_id or tokenizer.eos_token_id
pad_token_id = end_key_token_id or tokenizer.pad_token_id

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

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.

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_template.format(instruction=user_text)

    # Tokenize the user text.
    model_inputs = tokenizer(prompt_text, return_tensors="pt", **tokenizer_kwargs)

    # 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,
        pad_token_id=pad_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.

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 "", "", ""

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.

examples = [
    "Give me a recipe for pizza with pineapple",
    "Write me a tweet about the new OpenVINO release",
    "Explain the difference between CPU and GPU",
    "Give five ideas for a great weekend with family",
    "Do Androids dream of Electric sheep?",
    "Who is Dolly?",
    "Please give me advice on how to write resume?",
    "Name 3 advantages to being 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(
        "# Question Answering with " + model_id.value + " and OpenVINO.\n"
        "Provide instruction 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):
            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(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])

if __name__ == "__main__":
    demo.queue()
    try:
        demo.launch(height=800)
    except Exception:
        demo.launch(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/