Mamba Language Models are the new kids on the block in the AI world. They’re making waves with their efficiency and power, and we’re going to dive into the best of the best. So grab a cup of coffee, sit back, and let’s get started!
We’re going to check out the best of the best open-source models by comparing them on metrics like accuracy, speed, and ease of use. We’ll also discuss the pros and cons of each model.
Paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces
falcon-mamba-7b
Falcon-Mamba by tiiuae is a large language model (LLM) trained with 5,500 gigabytes of text from the Refined-Web dataset. The Refined-Web dataset is a large collection of web pages that has been filtered to remove duplicate content and low-quality pages. The Falcon-Mamba model is part of the Falcon suite of models, which are designed to increase the context length from 2,048 to 8,192 tokens.
The Falcon-Mamba-7b is the best mamba model right now. It’s open source, so you can use it for free. And because it’s based on the state-space model (SSM), it won’t take up more RAM if you give it longer context.
Mamba-Codestral-7B
Codestral Mamba is an open source code model based on the Mamba2 architecture. It was designed to achieve state-of-the-art performance on natural language processing tasks. The model is trained on a massive dataset of Python code and demonstrates competitive performance on a variety of tasks, including code generation, code translation, and code summarization.
Jamba-v0.1
AI21 Labs just dropped a super-sized AI model called Jamba-v0.1. It crushes similar models by handling a crazy amount of text (256K tokens!) on just one GPU.
Jamba is a biggie at 51B, but hear this – it mixes different cool architectures (like Transformers and stuff) to run efficiently. Plus, it’s open-source, so anyone can play around and adapt it! Jamba’s basically a powerful AI that’s free for anyone to mess with. This is a game-changer for AI!
Mamba Hermes 3B
This model is a remarkable example of the application of the Mamba state space model architecture, fine-tuned on the Hermes dataset.
The Hermes dataset is renowned in the field of language modeling for its comprehensive and diverse range of data, making it an excellent choice for training a language model to become a proficient assistant. The Mamba Hermes 3B, with its 3 billion parameters, leverages this dataset to its full potential.
One of the standout features of the Mamba Hermes 3B is its state space model architecture. This means that the model’s RAM usage remains constant, regardless of the number of conversations it handles. This is a significant advantage in terms of efficiency, especially in scenarios where the model needs to manage multiple conversations simultaneously.
Mamba Chat 2.8b
The Mamba-Chat model is an application of the Mamba state space model architecture. It’s not based on a transformer.
The model was fine-tuned on 16,000 samples of the ultrachat_200k dataset. This dataset is known for its extensive and diverse range of data.
In terms of usage, the Mamba-Chat model follows the zephyr format:
<|user|> {user_message}
<|assistant|> {assistant_message}
<|user|> {user_message}
<|assistant|> {assistant_message}This format allows for a clear and structured conversation between the user and the assistant.
Mamba Bagel 2.8b
The Mamba Bagel 2.8B is a fine-tuned model based on the Mamba state space model architecture.
The Mamba Bagel 2.8B model stands out for its fine-tuning on a unique dataset that is both open-source and uncensored. This dataset’s comprehensive and diverse nature allows the model to handle a wide range of topics and scenarios, making it versatile and robust. The uncensored nature of the dataset also means that the model is not limited by pre-defined restrictions, allowing for more natural and fluid interactions. This makes the Mamba Bagel 2.8B model particularly suited for applications that require a high degree of realism and adaptability.
