Choosing between PyTorch and TensorFlow

When it comes to choosing between PyTorch and TensorFlow, it really depends on your use case and what you value in a framework.

One advantage of TensorFlow is the ability to export a trained model into formats compatible with Tensorflow Lite or Tensorflow JS for mobile/web dev as well as edge device deployment, making it a great choice for those interested in mobile or edge computing. On the other hand, PyTorch's biggest advantage is the constant updates and being the go-to language for most of the new and exciting models. Additionally, PyTorch is popular in academia and research.

Despite TensorFlow's advantages, some developers prefer PyTorch for its simplicity and intuitive user experience. Others even prefer newer frameworks such as JAX, which is mostly Numpy with GPU acceleration and autograd.

In industry, TensorFlow is still commonly used, and while some developers have a love-hate relationship with it, they appreciate its computation-graph approach, and the fact that with TensorFlow 2, you can still compile your models as a graph for improved performance. Keras, which is often used with TensorFlow, is not loved by everyone due to its over-engineered nature, though its functional API is acceptable.

For those interested in JAX, the Equinox library makes building neural networks user-friendly, and it has been found to be faster and more composable than PyTorch. Check out this CNN on MNIST introductory example.

Ultimately, the choice between PyTorch and TensorFlow (or any other framework) will depend on your needs and preferences. It's worth experimenting with different frameworks to see which one best suits your needs.

PyTorch, TensorFlow, JAX, Equinox, neural networks, edge computing, academia, research, Keras

Building a PC for Large Language Models: Prioritizing VRAM Capacity and Choosing the Right CPU and GPU

Building a PC for running large language models (LLMs) requires a balance of hardware components that can handle high amounts of data transfer between the CPU and GPU. While VRAM capacity is the most critical factor, selecting a high-performance CPU, PSU, and RAM is also essential. AMD Ryzen 8 or 9 CPUs are recommended, while GPUs with at least 24GB VRAM, such as the Nvidia 3090/4090 or dual P40s, are ideal for … click here to read

LMFlow - Fast and Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Some recommends LMFlow , a fast and extensible toolkit for finetuning and inference of large foundation models. It just takes 5 hours on a 3090 GPU for fine-tuning llama-7B.

LMFlow is a powerful toolkit designed to streamline the process of finetuning and performing inference with large foundation models. It provides efficient and scalable solutions for handling large-scale language models. With LMFlow, you can easily experiment with different data sets, … click here to read

Building an AI-Powered Chatbot using lmsys/fastchat-t5-3b-v1.0 on Intel CPUs

Discover how you can harness the power of lmsys/fastchat-t5-3b-v1.0 language model and leverage Intel CPUs to build an advanced AI-powered chatbot. Let's dive in!

Python Code:

 # Installing the Intel® Extension for PyTorch* CPU version python -m pip install intel_extension_for_pytorch # Importing the required libraries import torch from transformers import T5Tokenizer, AutoModelForSeq2SeqLM import intel_extension_for_pytorch as ipex # Loading the T5 model and tokenizer tokenizer = T5Tokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0") model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0", low_cpu_mem_usage=True) # Setting up the conversation prompt prompt …
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LLaVA: Large Language and Vision Assistant

The paper presents the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, the authors introduce LLaVA, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

LLaVA demonstrates impressive multimodel chat abilities and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and … click here to read

Making the Right Choice: Intel i5-13600k vs. AMD Ryzen 7 7700X - A Closer Look

When it comes to selecting a processor for your PC, the decision can be overwhelming. Two popular options on the market right now are the Intel i5-13600k and the AMD Ryzen 7 7700X. Both CPUs offer great performance, but there are some key factors to consider before making a choice.

One aspect to look at is the price and quality of the motherboards that support these processors. Many users have praised the z690/z790 … click here to read

Exploring the Best GPUs for AI Model Training

Are you looking to enhance your AI model performance? Having a powerful GPU can make a significant difference. Let's explore some options!

If you're on a budget, there are alternatives available. You can run llama-based models purely on your CPU or split the workload between your CPU and GPU. Consider downloading KoboldCPP and assign as many layers as your GPU can handle, while letting the CPU and system RAM handle the rest. Additionally, you can … click here to read

Open Source Projects: Hyena Hierarchy, Griptape, and TruthGPT

Hyena Hierarchy is a new subquadratic-time layer in AI that combines long convolutions and gating, reducing compute requirements significantly. This technology has the potential to increase context length in sequence models, making them faster and more efficient. It could pave the way for revolutionary models like GPT4 that could run much faster and use 100x less compute, leading to exponential improvements in speed and performance. Check out Hyena on GitHub for more information.

Elon Musk has been building his own … click here to read

New Advances in AI Model Handling: GPU and CPU Interplay

With recent breakthroughs, it appears that AI models can now be shared between the CPU and GPU, potentially making expensive, high-VRAM GPUs less of a necessity. Users have reported impressive results with models like Wizard-Vicuna-13B-Uncensored.ggml.q8_0.bin using this technique, yielding fast execution with minimal VRAM use. This could be a game-changer for those with limited VRAM but ample RAM, like users of the 3070ti mobile GPU with 64GB of RAM.

There's an ongoing discussion about the possibilities of splitting … click here to read

What has changed in Transformer architecture?

There have been close to no improvements on the original transformer architecture . Different architecture are better at different tasks, and the training objective can also vary. There's a major error in the paper " Attention is All You Need " where they accidentally put the layer norms after the layers not before them. Putting attention layers and MLPs in parallel makes the model run much faster, but doesn't really affect performance. The original … click here to read

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