---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- mamba
- state-space-model
- ssm
- causal-lm
- pytorch
- pretrained
datasets:
- wikimedia/wikipedia
---

# Mamba-50M

A ~50M-parameter [Mamba](https://arxiv.org/abs/2312.00752) (selective state-space) causal
language model, **pretrained from scratch on English Wikipedia**. Mamba replaces the attention
mechanism of a Transformer with a selective state-space layer, giving linear-time sequence
processing instead of the quadratic cost of self-attention.

> **This is a base model: pretrained only.** It has **not** been fine-tuned, instruction-tuned,
> RLHF'd, or aligned in any way. It is a raw next-token predictor intended for research.

## Model details

| | |
|---|---|
| Architecture | Mamba (selective SSM) |
| Parameters | ~50M |
| Context length | 512 tokens |
| Tokenizer | [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b) (BPE, ~50k vocab) — the same tokenizer used by the original `state-spaces/mamba-*` models |
| Language | English |
| License | Apache-2.0 |

## Limitations

- **Pretrained only.** No fine-tuning, instruction tuning, or alignment. It does not follow instructions and has no safety filtering; it simply continues text.
- **Small.** At ~50M parameters it has limited fluency and reasoning; expect frequent hallucination and repetition.
- **English only.** Trained solely on English Wikipedia; other languages are out of distribution.
- **Domain-narrow.** Only Wikipedia was used as training data.

## Training data

Pretrained on the English subset of Wikipedia: over 3 million articles.

### Training procedure

| Hyperparameter | Value |
|---|---|
| Learning rate | 5e-4 |
| Sequence length | 512 |
| Batch size | 64 |
| Tokenizer | GPT-NeoX-20B (`EleutherAI/gpt-neox-20b`) |
| Optimizer | `AdamW` |
| LR schedule / warmup | `constant` / `10000` |
| Total tokens seen | `~ 2.5-2.9B` |
| Hardware | `2x Nvidia Quadro RTX 6000 24GB` |

## Evaluation

Evaluated on a held-out set of **10,000 Wikipedia articles** that were not seen during training.
The training and evaluation loss curves are shown below.

![Training and evaluation loss](loss_curves.png)

## Citation

If you use this model, please cite the Mamba paper:

```bibtex
@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}
}
```