mamba2-jax 1.1.2

Pure JAX/Flax NNX implementation of the Mamba2 state-space model with state caching, pretrained weight loading, and time-series forecasting.

Mamba2-JAX

A pure JAX/Flax NNX implementation of the Mamba2 state-space model with SSM state caching, pretrained weight loading from HuggingFace, causal language modeling, and time-series forecasting.

This is the standalone PyPI package for the Mamba2 implementation authored by Cosmo Santoni and merged into Google's JAX ML Bonsai library. The upstream source lives at bonsai/models/mamba2.

Independent community reimplementation — not affiliated with, endorsed by, or connected to the original Mamba authors or state-spaces/mamba.

Supported Models

Features

• Flax NNX modules (no legacy init/apply ceremony)
• SSM + convolution state caching for O(n) autoregressive generation
• Pretrained weight loading from HuggingFace (state-spaces/mamba2-130m, etc.)
• Causal language modeling (Mamba2ForCausalLM) with tied or untied embeddings
• Time-series forecasting (Mamba2Forecaster)
• Golden parity tests against the reference mamba_ssm PyTorch implementation
• Fully compatible with jax.jit, jax.grad, jax.vmap
• Runs on CPU, GPU (CUDA), and TPU

State Space Caching

The SSM state cache enables O(n) autoregressive generation instead of O(n^2) re-computation. The example below demonstrates a ~30x speedup on the 780M parameter model running on a TPU v6e when caching is enabled:

Installation

From PyPI

pip install mamba2-jax

From source

git clone https://github.com/jax-state-spaces/mamba2-jax.git
cd mamba2-jax
pip install -e ".[dev]"

For loading pretrained weights

pip install "mamba2-jax[pretrained]"

For GPU or TPU support, install the appropriate JAX backend as described in the JAX installation guide.

Usage

Causal Language Model

import jax.numpy as jnp
from flax import nnx
from mamba2_jax import Mamba2Config, Mamba2ForCausalLM

cfg = Mamba2Config(vocab_size=1024, hidden_size=256, num_hidden_layers=4,
                   state_size=64, head_dim=32, chunk_size=64)
model = Mamba2ForCausalLM(cfg, rngs=nnx.Rngs(0))

input_ids = jnp.ones((2, 64), dtype=jnp.int32)
outputs = model(input_ids, labels=input_ids)

print(outputs["logits"].shape)  # (2, 64, 1024)
print(float(outputs["loss"]))

Loading pretrained weights

from mamba2_jax import Mamba2ForCausalLM

model = Mamba2ForCausalLM.from_pretrained("state-spaces/mamba2-130m")

Cached generation

import jax.numpy as jnp
from mamba2_jax import Mamba2ForCausalLM, Mamba2Config

cfg = Mamba2Config.tiny()
model = Mamba2ForCausalLM(cfg, rngs=__import__("flax").nnx.Rngs(0))

# Prefill
prompt = jnp.array([[1, 2, 3]], dtype=jnp.int32)
out = model(prompt)
cache = out["cache"]

# Decode with cache (O(1) per step)
next_token = jnp.array([[4]], dtype=jnp.int32)
out = model(next_token, cache=cache)
print(out["logits"].shape)  # (1, 1, vocab_size)

Time-series forecasting

import jax.numpy as jnp
from mamba2_jax import Mamba2Forecaster, create_random_forecaster

model = create_random_forecaster(input_dim=10, d_model=256, n_layers=4,
                                  output_dim=1, forecast_horizon=24)

x = jnp.ones((8, 100, 10))  # (batch, seq_len, features)
y = model(x)
print(y.shape)  # (8, 24, 1)

Performance

Benchmarked on a TPU v6e with the state-spaces/mamba2-130m checkpoint:

Project Structure

mamba2-jax/
├── mamba2_jax/
│   ├── __init__.py          # Public API
│   ├── modeling.py          # Config, SSD kernel, all model classes
│   └── params.py            # Weight loading & parameter utilities
├── tests/
│   ├── test_mamba2.py       # Comprehensive test suite
│   ├── run_model.py         # Generation demo script
│   └── artifacts/           # Golden parity data
├── docs/                    # Benchmark figures
├── LICENSE                  # Apache 2.0
├── pyproject.toml
└── README.md

Contributing

Contributions are welcome! Areas where help is particularly valuable:

• Performance optimization and profiling
• Test coverage expansion
• Bug reports and feature requests

Please open an issue or submit a pull request on GitHub.

Acknowledgments

Original Mamba2 Authors:

• Tri Dao and Albert Gu for the Mamba2 architecture and the original mamba_ssm implementation
• The State Spaces team for advancing SSM research

JAX Ecosystem:

• The JAX, Flax, and Optax teams at Google for the excellent frameworks

License

This project is licensed under the Apache License 2.0.

Citation

If you use this implementation in your research, please cite the original Mamba2 paper and this JAX implementation:

@inproceedings{mamba2,
  title={Transformers are {SSM}s: Generalized Models and Efficient Algorithms
         Through Structured State Space Duality},
  author={Dao, Tri and Gu, Albert},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2024}
}

@software{mamba2jax,
  author  = {Cosmo Santoni},
  title   = {mamba2-jax: Pure JAX Implementation of Mamba2},
  year    = {2025},
  url     = {https://github.com/jax-state-spaces/mamba2-jax}
}