Pure Rust implementation of Mamba [1] inference with minimal dependencies. Mamba is an alternative to the transformer architecture. It leverages State Space Models (SSMs) with the goal of being computationally efficient on long sequences.

• [1]. Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Most of the inspiration for mamba.rs (and some of the code) come from llama2.rs (by @srush).

# Download the tokenizer configs.
wget https://huggingface.co/EleutherAI/gpt-neox-20b/raw/main/vocab.json
wget https://huggingface.co/EleutherAI/gpt-neox-20b/raw/main/merges.txt

# Download and generate the weight files.
python get_weights.py

# Run with a prompt.
cargo run --release -- "Mamba is"

By default this uses the 130m model. As it is a really small model, the generated text might not be great.

Alternatively to use the 370m model instead of the 130m one, use the following.

python get_weights.py --which 370m
cargo run --release -- "Mamba is" --which 370m

Other variants are 790m, 1.4b, and 2.4b.

Current features:

• Model weight loading using mmap.
• Matrix multiplication using rayon to leverage multiple cores, but not cache friendly.

TODO:

• Improve the efficiency by using SIMD instructions (or fast-math) and more parallelism.
• Quantized versions?