Aziz et al. Paper Summaries

Machine Learning Engineer @ SoundCloud | Sharing My learnings About AI Research. ✨ Follow for updates. https://www.linkedin.com/in/mohamed-aziz-belaweid/

The hottest Substack posts of Aziz et al. Paper Summaries

And their main takeaways

Does ZEPHYR 7B Outperform 70B LLama-2 Chat?

495 implied HN points • 13 Dec 23
  1. Zephyr 7B model by HuggingFace outperforms LLama-2 Chat on MT-Bench dataset.
  2. Zephyr 7B uses techniques like Distilled Supervised Finetuning, AI Feedback, and Distilled Direct Policy Optimization for its training process.
  3. Zephyr 7B excels in alignment but may not surpass 70B LLama-2 Chat in terms of deep knowledge.

Phi-3, your Pocket LLM!

79 implied HN points • 29 Apr 24
🕹 Technology AI Machine Learning Hardware Software Data science
  1. Microsoft's Phi-3 is a new AI model that is small enough to run on your phone, yet still performs well. This is a big deal because most AI models are too large for personal devices.
  2. The model uses high-quality, filtered data for training, focusing on reasoning and educational materials. This approach makes Phi-3 better at understanding rather than just memorizing facts.
  3. Even though Phi-3 is powerful, it has some limitations, like not being multilingual. There are also tasks it struggles with, like those needing lots of factual knowledge.

Complete Summary of Absolute, Relative and Rotary Position Embeddings!

79 implied HN points • 31 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Computing Data science
  1. Transformers can't understand the order of words, so position embeddings are used to give them that context.
  2. Absolute embeddings assign unique values to each word's position, but they struggle with new positions beyond what they trained on.
  3. Relative embeddings focus on the distance between words, which makes the model aware of their relationships, but they can slow down training and searching.

Dissecting OLMo, The Most Open Source LLM Paper!

79 implied HN points • 06 Mar 24
🕹 Technology AI Models Open Source Data processing Machine Learning
  1. OLMo is a fully open-source language model. This means anyone can see how it was built and can replicate its results.
  2. The OLMo framework includes everything needed for training, like data, model design, and training methods. This helps new researchers understand the whole process.
  3. The evaluation of OLMo shows it can compete well with other models on various tasks, highlighting its effectiveness in natural language processing.

DoRA is The New LoRA!

59 implied HN points • 07 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Software Development
  1. LoRA helps fine-tune large language models without changing all their parameters. It uses two small matrices, which keeps the performance quick during use.
  2. LoRA's updates to weights can miss valuable details you'd get from full fine-tuning, because it treats magnitude and direction together.
  3. DoRA improves on LoRA by separating magnitude and direction, leading to better performance on reasoning tasks and other applications. It works best with smaller settings, making it efficient.
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Is Step Back Prompting The Best Prompting Strategy?

59 implied HN points • 20 Mar 24
🕹 Technology AI Machine Learning Data science Computing Engineering
  1. Step Back Prompting helps models think about big ideas before answering questions. This method shows better results than other prompting techniques.
  2. Even with Step Back Prompting, models still find it tricky to put all their reasoning together. Many errors come from the final reasoning step which can be complicated.
  3. Not every question works well with Step Back Prompting. Some questions need quick, specific answers instead of a longer thought process.

What Is SwiGLU? How to Implement It? And Why Does it Work?

59 implied HN points • 13 Mar 24
🕹 Technology AI Machine Learning Software Development Neural Networks Deep Learning
  1. SwiGLU is a type of activation function used in deep learning. It's a mix of two parts: the Swish function and Gated Linear Units, which helps models learn better patterns.
  2. To implement SwiGLU, you can use a straightforward code in Pytorch that combines linear transformations with the Swish function. This makes it easier for neural networks to handle complex data.
  3. The exact reason why SwiGLU works so well is not fully understood yet. Researchers are still exploring why this approach gives better results in certain models.

Chameleon, Meta's Mixed-Modal Foundation Model

19 implied HN points • 02 Jun 24
🕹 Technology AI Models Machine Learning Deep Learning Data processing Tokenization
  1. Chameleon combines text and image processing into one model using a unique architecture. This means it processes different types of data together instead of separately like previous models.
  2. The training of Chameleon faced challenges like instability and balancing different types of data, but adjustments like normalization helped improve its training process. It allows the model to learn effectively from both text and images.
  3. Chameleon performs well in generating responses that include both text and images. However, just adding images didn't harm the model's ability to handle text, showing it can work well across different data types.

How To Create Automatic Music Playlists With RL?

59 implied HN points • 09 Sep 23
  1. Creating automatic music playlists using Reinforcement Learning (RL) helps personalize music recommendations based on user preferences and context.
  2. Current methods like Collaborative Filtering and Sequence Modeling have limitations in understanding user preferences and adapting to changing preferences over time.
  3. Reinforcement Learning (RL) offers a solution by training a user behavior model to predict user satisfaction with recommended tracks, leading to higher playlist personalization and user satisfaction.

Your Complete Guide to RCNN, Fast-RCNN, Faster-RCNN and Mask-RCNN

39 implied HN points • 24 Sep 23
  1. The RCNN family involves different architectures for object detection, each solving obstacles like huge time consumption and fixed algorithms.
  2. Fast-RCNN improves over RCNN by reducing classification time and introducing ROI pooling instead of SVM classifiers.
  3. Faster-RCNN further enhances Fast-RCNN by proposing a Region Proposal Network, reducing the reliance on Selective Search and allowing for shared computations within the network.

Reinforcement Learning With AI Feedback Explained!

39 implied HN points • 16 Sep 23
  1. Reinforcement learning with human feedback does not scale, as it requires expert human labelers and is time-consuming and expensive.
  2. Using another large language model (LLM) for feedback can replace human feedback in reinforcement learning, but some challenges remain in prompt design and model size.
  3. Evaluation metrics like AI labeler alignment and pairwise accuracy help compare the performance of models trained with human feedback versus AI feedback.

Cloze-Driven Pretraining of Self-Attention Networks Summary

19 implied HN points • 29 Sep 23
  1. Introduces a new pre-training method for transformers improving language understanding
  2. Significant performance gains on GLUE tasks and new SOTA in NER and parsing
  3. Model architecture involves two self-attention towers, block structures, and fine-tuning details

How Speculative Sampling Can Increase Your LLM's Inference Speed!

3 HN points • 07 Oct 23
  1. LLMs are slow due to autoregressive nature, memory bandwidth issues, and communication overhead.
  2. Speculative sampling involves using a smaller 'draft model' to generate tokens quickly and pass them to a larger 'target model'.
  3. This method helps speed up LLM inference by skipping forward passes on easy-to-predict tokens.

Coming soon

0 implied HN points • 05 Sep 23
  1. Aziz Belaweid is launching a Substack newsletter.
  2. The newsletter will feature paper summaries.
  3. Readers can subscribe to stay updated.