Run Code Llama 13b Gguf Model On Cpu: Gguf Is The New Ggml

The AI Anytime Channel is back with another exciting video that delves into the world of cutting-edge technology. In this edition, we'll be exploring how to inference the newly released Code Llama model using a revolutionary file format called GGUAV. Get ready to level up your language model inference game!

What is GGUAV and Why Should You Care?

GGUAV, short for "Grammarly Great Unified Archive Format," is a unified file format that has taken the AI community by storm. It allows for seamless inference of language models on a wide range of hardware. Gone are the days of dealing with cumbersome dot bin files. GGUAV brings extensibility and ease-of-use to a whole new level.

Previously, we primarily relied on GGML and GPTQ file formats for language model inference. However, with the advent of GGUAV, we now have a game-changer on our hands. The best part? Various frameworks and bindings, such as Llama CPP and Kobold AI, have started supporting GGUAV, making it accessible to a broader audience.

Unleashing the Power of Code Llama with GGUAV

In the video, we witness the remarkable abilities of the Code Llama 13B GGUAV model. This model is specifically designed for natural language queries and retrieves code information with unrivaled precision. By leveraging GGUAV, we not only tap into the model's power, but we also gain the advantage of higher extensibility.

Creating a simple application using C Transformers, a set of dependencies needed to use GGUAV, is demonstrated in the video. With C Transformers, inference of the 13B model on a CPU machine becomes a breeze. The AutoModel for CausalLM is used for text generation, and the GGUAV file format is employed to specify the model file path. Additionally, you have the flexibility to customize prompts without worrying about unnecessary parameters.

Step-by-Step Guide to GGUAV Model Inference

Let's break down the video's step-by-step guide on how to inference the Code Llama 13B model using the GGUAV file format. Make sure you're equipped with the latest version of C Transformers (0.2.24) to ensure seamless integration. Now, let's dive right in:

1. Set the inference hyperparameters: Begin by configuring parameters such as "Max new tokens" and "repetition penalty" to precisely control the behavior of your model.

2. Load the pre-trained language model: Define a function called "load_llm" to load the powerful Code Llama 13B GGUAV model. The model file is conveniently downloaded from a specific location and simply placed in your project. Estimating the file size to be around 12GB, get ready to witness the might of this beast!

3. Define the inference function: Create a function called "llm_function" that takes user input and generates a response using the loaded language model. The resulting response is stored in a variable called "response," which is then returned as the output text. Simple, yet powerful!

4. Transforming your application into a Gradu app: By utilizing the "chat_interface" class, set up a Gradu app with the title "Code Llama 13B GGUAV Demo." Customize your app with default prompts and any additional features to suit your needs.

5. Launch and interact with the app: It's time to run the app! Launch it and witness the magic unfold. Interact with the chat interface, entering prompts and receiving impressive model-generated responses. Get creative, ask questions, and see the Code Llama in action!

By following these instructions and using the recommended dependencies, you'll be able to successfully inference the Code Llama 13B model using GGUAV on your CPU machines. The possibilities are endless!

The Journey Continues: From Error to Success

In the provided code snippet, we glimpse into the author's personal experience running a Python app and the challenges they faced. At first, they encounter an unexpected keyword argument error, which sets them on a mission to correct their mistake. After making the necessary correction, they triumphantly run the app and are greeted with a sleek and user-friendly chat interface provided by Gradio.

The chat interface captivates with its simplicity and functionality, consisting of a text input box, buttons for retry, undo, and clear, and even some example prompts. With the app up and running, the author takes full advantage of the interface to ask questions and receive valuable model-generated responses.

The first question revolves around connecting with an SQL database and listing all the tables using Python code. The app, taking a moment to process the request, delivers a response that includes the sought-after code snippet. Excited by this outcome, the author decides to explore further and asks a question about training a linear regression model using Cycle. Once again, the app processes the request, eventually providing the author with the code required to train the model.

The author notes the versatility of the app and contemplates the potential to expand its capabilities. Thanks to the GGWave platform, the Code Llama 13B model can now run on CPU machines and tackle a wide array of tasks. The results obtained using a 13B model on a CPU are impressive, but the author also highlights the availability of GPU support through CTransformers. They emphasize the importance of proper setup and mention various configuration options, such as the number of GPU layers and the context length.

To explore even further, the author directs our attention to the Hugging Face repository, where an abundance of models has been converted to the GGUA format. Their mention of the Widget folder, which has fared admirably on human evaluation leaderboards, piques our interest. Should you wish to delve deeper into the converted models, detailed code and resources await you at the repository.

In closing, the author shares their fascinating journey of using the Python app with the GGWave environment. The app, coupled with the chat interface, showcases the outstanding functionality of the Code Llama model while delivering prompt and accurate code snippets. They remind us of the possibiliti