mamba-ssm-macos 1.0.2

Mamba SSM - State Space Models optimized for Apple Silicon

Mamba SSM for macOS Apple Silicon

Mamba 1 and Mamba 2 State Space Models for Apple Silicon

Training and inference of Mamba 1 & 2 on Apple Silicon with MPS acceleration. Works without CUDA/Triton.

Installation

pip install mamba-ssm-macos

Prerequisites: macOS 12.3+ with Apple Silicon, Python 3.10+, 8GB+ RAM recommended.

Quickstart

import torch
from transformers import AutoTokenizer
from mamba_ssm import MambaLMHeadModel, generate_text_with_model, get_device

device = get_device()
model_name = "state-spaces/mamba-130m"  # or "state-spaces/mamba2-130m"
model = MambaLMHeadModel.from_pretrained(model_name, device=device)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
text = generate_text_with_model(model, tokenizer, "Once upon a time", device, max_length=50, temperature=0.8)
print(text)

The model is downloaded from Hugging Face Hub on first run and cached under ~/.cache/huggingface/.

Training

import torch
from torch import nn
from mamba_ssm.modules.mamba2 import Mamba2

model = nn.Sequential(nn.Embedding(1000, 128), *[Mamba2(d_model=128, d_state=64, d_conv=4, expand=2, headdim=64, ngroups=1, chunk_size=256, device='mps') for _ in range(2)], nn.LayerNorm(128), nn.Linear(128, 1000, bias=False)).to('mps')
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

for input_ids, labels in dataloader:
    optimizer.zero_grad()
    logits = model(input_ids)
    loss = criterion(logits.view(-1, logits.size(-1)), labels.view(-1))
    loss.backward()
    optimizer.step()

See examples/03_training.py for a complete training example.

Examples

Clone the repo to run examples:

git clone https://github.com/purohit10saurabh/mamba-ssm-macos.git
cd mamba-ssm-macos
uv sync
python -m examples.01_core_modules  # Core modules usage
python -m examples.02_text_generation --interactive  # Text generation
python -m examples.03_training  # Training

Troubleshooting

"MPS not available" — Check with python -c "import torch; print(torch.backends.mps.is_available())". The library falls back to CPU automatically.

Slow first run — Initial from_pretrained downloads ~500MB of weights from Hugging Face Hub. Subsequent runs use the local cache.

Development

git clone https://github.com/purohit10saurabh/mamba-ssm-macos.git
cd mamba-ssm-macos
uv sync --extra dev
make test

Available make targets: test, test-unit, test-integration, test-quick, format, format-check, clean. See the Makefile for details.

Citation

Also available via GitHub's "Cite this repository" button (CITATION.cff).

@software{purohit2026mamba_ssm_macos,
  title={Mamba SSM for macOS Apple Silicon},
  author={Purohit, Saurabh},
  year={2026},
  url={https://github.com/purohit10saurabh/mamba-ssm-macos}
}

Original Mamba papers

@article{gu2023mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}

@article{dao2024transformers,
  title={Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality},
  author={Dao, Tri and Gu, Albert},
  journal={arXiv preprint arXiv:2405.21060},
  year={2024}
}

References

• state-spaces/mamba — Original implementation
• state-spaces/mamba1-130m — Mamba 1 130M pre-trained model
• state-spaces/mamba2-130m — Mamba 2 130M pre-trained model

Contributing

Feel free to open an issue or submit a PR. See Development for the local setup.

License

Apache 2.0 — see LICENSE.