The AI community is actively engaging with Mamba, a new language model architecture based on state-space models (SSMs), which is seen as a competitor to the Transformer architecture. Developers Albert Gu and Tri Dao have created a minimal implementation of Mamba in a single file of PyTorch, which has been notably implemented in just 300 lines of code. The model's support has been integrated into lm-evaluation-harness, allowing for benchmarking against the Pythia model. Users are being encouraged to learn how to train Mamba on their own data through a session called Practical ML Dive, and five resources have been provided to help understand SSMs and Mamba.
Are you wondering how the new Mamba language model works? Mamba is based on state-space models (SSMs), a new competitor to the Transformer architecture. Here are 5 resources to help you learn about SSMs & Mamba! ↓↓↓
Support for Mamba has landed in lm-evaluation-harness! Use it with `--model mamba_ssm` : https://t.co/4dTLhmWbtP Was really happy to see @_albertgu @tri_dao provide support for our new release natively alongside their architecture code, to benchmark against Pythia reproducibly! https://t.co/Or7NurSD5n
Nice job implementing Mamba in 300 lines of code!! https://t.co/IiNYibEHLQ
Today we're continuing with Mamba! - How to train Mamba on your own data! See you in the Practical ML Dive soon https://t.co/3fbdg1DLAL https://t.co/nBDMjWpOMC
Minimal implementation of Mamba, the new LLM architecture from @_albertgu and @tri_dao, in one file of PyTorch https://t.co/SeoDcakm6V