Introduce your own AI chat friend

All right, you tech-savvy guy, get ready for an exciting adventure in the land of artificial intelligence! We’re not just dipping our toes here; let’s dive headfirst into the deep end with the Qwen Chat model. What’s on the agenda? Setting up a chatbot smarter than a fox and respecting privacy like a top secret agent. Intrigued? You should be! Let’s start our journey by understanding Generative AI and LLM (Large Language Model).

Generative AI

Generative artificial intelligence refers to the branch of artificial intelligence focused on creating new content, whether it is text, images, music or other forms of media. This type of artificial intelligence uses machine learning models, especially generative models, to understand patterns, features, and relationships in large data sets and generate results that are novel and often indistinguishable from human-generated content.

Types of generative models

• Generative adversarial networks (GAN): A type of neural network architecture in which two models (generator and discriminator) are trained simultaneously. The generator creates new instances of the data while the discriminator evaluates them. The process results in increasingly convincing results.
• Variational autoencoders (VAE): These models generate new instances similar to the input data. They are often used in creating images.
• transformers: Originally designed for NLP tasks, transformer models like GPT (Generative Pretrained Transformer) can generate coherent and contextually relevant text. They are also adapted for generative tasks for other types of data.

Applications

• Content creation: Generative AI can produce original artwork, write stories or articles, compose music, and create virtual environments for games and simulations.
• Data augmentation: It can generate additional training data for machine learning models, helping to improve their accuracy and robustness.
• Personalization: Algorithms can tailor content to individual preferences, improving user engagement.
• Discovery of the drug: Generative models can suggest new molecular structures for drugs that might be effective against specific diseases.

Challenges

• Quality control: Ensuring that the generated content meets quality standards and that there is no bias present in the training data.
• Computer requirements: Training generative models often requires significant computing power and large datasets.
• Interpretability: Understanding how these models make decisions and generate results can be challenging, affecting trust and reliability.

Generative AI continues to develop rapidly, and its capabilities push the boundaries of what machines can create, offering both exciting opportunities and challenges that need to be managed responsibly.

LLM

What are large language models (LLM)? They are a type of artificial intelligence based on deep learning techniques that are designed to understand, generate and work with human language. They are called “big” because they consist of many millions or even billions of parameters that allow them to capture a wide variety of linguistic nuances and contexts.

LLMs are trained on massive amounts of text data and use architectures such as transform neural networks, which have the ability to process sequences of data (like sentences) and pay attention to different parts of the sequence when making predictions. This makes them particularly effective for a range of natural language processing (NLP) tasks, such as:

• Text generation: LLMs can write essays, create poetry or generate code based on the prompts they are given.
• Translation: They are able to translate text between different languages ​​with a high degree of accuracy.
• Answer to the question: LLMs can answer questions by understanding context and extracting information.
• Abstract: They can condense long documents into concise summaries.
• Sentiment Analysis: LLMs can determine the sentiment behind a text, such as recognizing whether a review is positive or negative.

Why Qwen? Short review

Are you looking for an AI that can talk, create content, compress, encode and more, while respecting your right to privacy? Look no further; The Qwen Chat model is here to transform your data center into a bastion of secure interactions powered by AI.

Qwen is not your average chatbot. It is built on a massive language model and is trained on a whopping 3 trillion tokens of multilingual data. This AI marvel has an intricate understanding of both English and Chinese and is fine-tuned for human-like interaction.

Why go local with Qwen?

Deploying Qwen locally on your server means taking control. It’s about keeping the conversations you have, the data processed and the privacy promised to you. Whether you are a company looking to integrate an intelligent chat system, a developer interested in AI research, or simply an enthusiast looking to explore the frontiers of conversational AI, Qwen is your choice.

Now, why would you want to host this LLM locally? Three words: control, speed and privacy. Keep your information at your fingertips, answers come lightning fast and you can rest easy knowing your chatbot isn’t babbling your secrets around public services.

Open source and community driven

The spirit of innovation in artificial intelligence is enhanced by the open source community. In keeping with this tradition, the full source code for the Qwen Chat Model is available on GitHub for anyone interested in diving into the mechanics of the model, contributing to its development, or simply using it as a learning resource. Whether you’re a researcher, developer, or AI hobbyist, you can access the source code on Qwen.

Before you start: The basics

Before we set sail on this technological odyssey, let’s make sure you have all your ducks in a row:

• A Linux server with a GPU card – because, let’s face it, speed matters.
• Python 3.6 or later – the magic wand of programming.
• pip or Anaconda – your handy package managers.
• Git (optional) – for those who like their code served fresh from the repository.
• NVIDIA drivers, CUDA Toolkit and cuDNN are the holy trinity for GPU acceleration.

Did you figure it all out? Incredible! Let’s get our hands dirty (figuratively, of course).

Creating Conversations: Where to Run Your Python Code

Whether you’re a loyal Visual Studio Code fan, a PyCharm enthusiast, or someone who enjoys the interactive style of Jupyter Notebooks, Python code for talking to Qwen is flexible and IDE-independent. All you need is a Python-enabled environment and you’re ready to bring your AI chat friend to life.

Here’s a pro tip: If you use VSCode, take advantage of the built-in terminal to seamlessly run your Python scripts. Just open the command palette (Ctrl+Shift+P), type Python: Run Python File in Terminal and let VSCode do the heavy lifting. You will see Qwen’s responses directly in your integrated terminal.

For those of you who prefer PyCharm, running your code is just as smooth. Right-click your script and select Run ‘script_name.py’ and watch the IDE execute your conversation with Qwen. PyCharm’s powerful tools and debugging features make it an excellent choice for developing more complex interactions.

And it doesn’t end there – there are a whole host of IDEs and code editors that embrace Python with open arms. Choose the one that best suits your workflow and start chatting!

Trade setup: environment

First things first: Let’s prepare your Linux server. Make sure your package list is as fresh as the morning breeze and Python and pip are ready to do their magic:

sudo apt update
sudo apt install python3 python3-pip

Now for the secret ingredient: the virtual environment. It’s like having a personal workspace where you can make a mess without someone yelling at you to clean up:

pip install --user virtualenv
virtualenv qwen_env
source qwen_env/bin/activate

Toolbox: Installing Dependencies

Before we bring Qwen to life, you’ll need some tools. Think of this as gathering the ingredients for a Michelin-starred meal:

pip install torch torchvision torchaudio
pip install transformers

Don’t forget to match PyTorch with your CUDA version – it’s like pairing good wine with real cheese.

Waking up Qwen: Model initialization

They speak the same language: Tokenizer

Words are just words until Qwen gives them meaning. That’s where the tokenizer comes in, turning your thoughts into something Qwen can chew on:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)

The brain of surgery: a model

Qwen’s mind is vast and ready to be filled with your conversations. Here’s how to wake up a sleeping giant:

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()

Depending on your hardware, you can opt for different precision modes such as BF16 or FP16. It’s like tuning your guitar for that perfect tone.

Engaging in continuous dialogue with Qwen

Now comes the exciting part – it’s time to talk to Qwen! But before you get carried away with the back and forth, let’s talk about something crucial: the art of conversational continuity.

Here’s a quick look at the type of response you can expect:

response, history = model.chat(tokenizer, "Greetings, Qwen! How's life in the digital realm?", history=None)
print("Qwen:", response)

In our opening gambit, we welcome Qwen with no strings attached – that is, no chat history. By setting history=None, we tell Qwen, “This is the beginning of our conversation.” Qwen will respond with the freshness of a new interaction with nothing but an immediate prompt to continue.

Now watch the context magic unfold:

response, history = model.chat(tokenizer, "Any thoughts on the meaning of life, the universe, and everything?", history=history)
print("Qwen:", response)

In this round we pass on the history we got from our previous exchange. This is like handing Qwen a journal of everything we’ve talked about so far. With this historical context, Qwen can craft a response that is not only witty or profound, but also relatable to our ongoing conversation. It’s the difference between chatting with a wise friend who knows you and asking a stranger a question.

• Why “history” is important: Think of history as a thread that connects the pearls of our conversation. Without it, each Qwen answer would be an isolated pearl, beautiful but lonely. Along with the history, each pearl is tightly bound to the last, creating a beautiful and cohesive series of dialogues. Context is king in conversation, and history is the bearer of context.
• Keeping the conversation flowing: Just like in human interactions, referring to past comments, jokes, or stories makes for an interesting joke. Qwen, armed with conversation history, can recall and reference past conversations, allowing for conversation that is as continuous as it is captivating.

Ready, Set, Converse!

Now that you’re an expert on the importance of context with the history parameter, fire up that demo script and get ready to have an interesting conversation with Qwen. Whether you’re debating the cosmos or the best digital cookie recipe, Qwen is ready to follow your conversational lead with all the grace of a seasoned conversationalist.

Also, you can run that script and start a conversation. It’s like opening Pandora’s box, but instead of chaos you get wonderful banter:

And there you have it, my friend – you have your own personal AI chat buddy, ready to conquer the chat world.

Engage With Qwen: Demo code on GitHub

For those who want to dive in and start a conversation with Qwen, there’s a hands-on demo showing how to interact with the model. The demo code can be found on GitHub, which provides a practical example of how to use the Qwen Chat Model for a conversation. The code is designed to be clear and easy to use, allowing you to experience the capabilities of Qwen first hand.

To try the demo, visit the GitHub repository at Awesome-Qwen and explore the examples directory. Here’s how you can clone the repository and run the demo:

# Clone the repository
git clone https://github.com/k-farruh/Awesome-Qwen.git

# Navigate to the repository
cd Awesome-Qwen

# Install the necessary dependencies (if you haven't already)
pip install -r requirements.txt

# Run the demo script
python qwen_chat.py

Conclusion: the grand finale

Congratulations! You’ve navigated the treacherous waters of AI implementation like a seasoned captain. Now Qwen sits comfortably on your server and your data is as safe as home.

Explore the possibilities of Qwen, contribute to its development, and join a community of like-minded people who are passionate about advancing the state of AI conversation. Check out the Awesome-Qwen GitHub Repository for more information and to get started.

So go ahead and engage in epic dialogues with your awesome new AI sidekick. And who knows? Perhaps Qwen will surprise you with her digital wisdom or a joke that will make you ROFL.