Mamba-3 SISO 893M

mamba3-siso-893m is a pretrained causal language model with 893M parameters. It is built from stacked blocks, each containing a Mamba-3 SISO mixer followed by a gated MLP. It contains no attention layers and is released with BF16 weights in the public mamba_ssm checkpoint format.

Model architecture

The model was pretrained on 100B tokens from FineWeb-Edu. It uses tied input and output embeddings and the meta-llama/Llama-3.1-8B tokenizer.

Installation

Install CUDA-enabled PyTorch first, followed by the latest Mamba source:

pip install git+https://github.com/state-spaces/mamba.git --no-build-isolation

While this repository is private, authenticate with Hugging Face:

hf auth login

Usage

import torch
from transformers import AutoTokenizer
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

model_id = "state-spaces/mamba3-siso-893m"

tokenizer = AutoTokenizer.from_pretrained(
    "meta-llama/Llama-3.1-8B",
)

model = MambaLMHeadModel.from_pretrained(
    model_id,
    device="cuda",
    dtype=torch.bfloat16,
)
model.eval()

input_ids = tokenizer(
    "Mamba-3 is",
    return_tensors="pt",
).input_ids.cuda()

with torch.inference_mode():
    logits = model(input_ids).logits

print(logits.shape)

References

• Mamba-3: Improved Sequence Modeling using State Space Principles
• Official Mamba implementation

Citation

If you use this model, please cite:

@misc{lahoti2026mamba3improvedsequencemodeling,
  title         = {Mamba-3: Improved Sequence Modeling using State Space Principles},
  author        = {Aakash Lahoti and Kevin Y. Li and Berlin Chen and
                   Caitlin Wang and Aviv Bick and J. Zico Kolter and
                   Tri Dao and Albert Gu},
  year          = {2026},
  eprint        = {2603.15569},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2603.15569}
}