Mamba: Linear-Time Sequence Modeling with Selective State Spaces

In the ever-evolving landscape of deep learning, a new contender has emerged – Mamba. This linear-time sequence modeling approach is causing quite a stir in the community, promising efficient computation and groundbreaking results.

Some have speculated that Mamba could be the game-changer, while others were skeptical, citing comparisons with well-established transformers.

For those unfamiliar with Mamba, a detailed exploration and practical experiment insights were shared in this blog post. The author delves into the nuances of Mamba's performance in question-answering tasks, providing a comprehensive overview of the challenges and potential avenues for improvement.

The community's response has been diverse, with some highlighting promising results from casual experiments, while others emphasize the need for Mamba to have its "Bert moment" to truly rival transformers.

The technical aspects of Mamba are not overlooked either. A closer look at the code implementations reveals the intricate workings of "Linear-Time Sequence Modeling with Selective State Spaces." Those eager to contribute or explore further can find the relevant code here.

Whether Mamba will surpass the dominance of transformers remains an open question. Some argue that Mamba's selective state spaces provide a unique advantage, allowing for more efficient handling of long contexts compared to traditional attention mechanisms.

As we eagerly await more experiments and results, it's clear that the landscape of deep learning is in a state of constant evolution, and Mamba is a name we should watch closely.

Similar Posts

Discussion on Parallel Transformer Layers and Model Performance

The recent discussion raises important concerns about the lack of key paper citations, particularly regarding the parallel structure in Transformer layers. It's worth noting that this concept was first proposed in the paper "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning" (see Formula 2). Further, the notion of merging linear layers of the MLP and self-attention to enhance time efficiency was discussed in Section 3.5.

One of the points in the discussion is the … click here to read

Biased or Censored Completions - Early ChatGPT vs Current Behavior

I've been exploring various AI models recently, especially with the anticipation of building a new PC. While waiting, I've compiled a list of models I plan to download and try:

  • WizardLM
  • Vicuna
  • WizardVicuna
  • Manticore
  • Falcon
  • Samantha
  • Pygmalion
  • GPT4-x-Alpaca

However, given the large file sizes, I need to be selective about the models I download, as LLama 65b is already consuming … click here to read

WizardLM: An Efficient and Effective Model for Complex Question-Answering

WizardLM is a large-scale language model based on the GPT-3 architecture, trained on diverse sources of text, such as books, web pages, and scientific articles. It is designed for complex question-answering tasks and has been shown to outperform existing models on several benchmarks.

The model is available in various sizes, ranging from the smallest version, with 125M parameters, to the largest version, with 13B parameters. Additionally, the model is available in quantised versions, which offer improved VRAM efficiency without … 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

Stack Llama and Vicuna-13B Comparison

Stack Llama, available on the TRL Library, is a RLHF model that works well with logical tasks, similar to the performance of normal Vicuna-13B 1.1 in initial testing. However, it requires about 25.2GB of dedicated GPU VRAM and takes approximately 12 seconds to load.

The Stack Llama model was trained using the StableLM training method, which aims to improve the stability of the model's training and make it more robust to the effects of noisy data. The model was also trained on a … click here to read

LLAMA-style LLMs and LangChain: A Solution to Long-Term Memory Problem

LLAMA-style Long-Form Memory (LLM) models are gaining popularity in solving long-term memory (LTM) problems. However, the creation of LLMs requires a fully manual process. Users may wonder whether any existing GPT-powered applications perform similar tasks. A project called gpt-llama.cpp, which uses llama.cpp and mocks an OpenAI endpoint, has been proposed to support GPT-powered applications with llama.cpp, which supports Vicuna.

LangChain, a framework for building agents, provides a solution to the LTM problem by combining LLMs, tools, and memory. … click here to read

Extending Context Size in Language Models

Language models have revolutionized the way we interact with artificial intelligence systems. However, one of the challenges faced is the limited context size that affects the model's understanding and response capabilities.

In the realm of natural language processing, attention matrices play a crucial role in determining the influence of each token within a given context. This cross-correlation matrix, often represented as an NxN matrix, affects the overall model size and performance.

One possible approach to overcome the context size limitation … click here to read

Improving Llama.cpp Model Output for Agent Environment with WizardLM and Mixed-Quantization Models

Llama.cpp is a powerful tool for generating natural language responses in an agent environment. One way to speed up the generation process is to save the prompt ingestion stage to cache using the --session parameter and giving each prompt its own session name. Furthermore, using the impressive and fast WizardLM 7b (q5_1) and comparing its results with other new fine tunes like TheBloke/wizard-vicuna-13B-GGML could also be useful, especially when prompt-tuning. Additionally, adding the llama.cpp parameter --mirostat has been … click here to read

© 2023 All rights reserved.