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sahilfarib/mamba3-bangla-case-study
mamba3-bangla-case-study is a machine learning model from sahilfarib. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
These are the checkpoints for the MRL 2026 paper State Tracking vs. Attention in a Morphologically Rich Low-Resource Language: A Controlled Bangla Case Study with Mamba-3 (Farib, Meem, Islam, Arefin, and Anik).
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Updated Sep 26, 2026
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From the Hugging Face model README
These are the checkpoints for the MRL 2026 paper State Tracking vs. Attention in a Morphologically Rich Low-Resource Language: A Controlled Bangla Case Study with Mamba-3 (Farib, Meem, Islam, Arefin, and Anik).
The repository has 15 causal language models, each trained from scratch on 1B tokens of
Bangla (FineWeb-2 ben_Beng). Seeds are 1337, 2024, 3419, 5150, and 8888. All models are
matched at about 24.5M non-embedding parameters and share a 32k SentencePiece vocabulary.
| Folder | Architecture |
|---|---|
transformer_seed<N>/ | Llama-style Transformer (RoPE, SwiGLU, full attention) |
mamba3_seed<N>/ | Tower of the official Mamba-3 SISO block (state-spaces/mamba) |
hybrid_seed<N>/ | Mamba-3 tower with RoPE attention at 2 of its 15 layers |
Each folder has a checkpoint.pt and the matching config.json. checkpoint.pt holds a
dict with the keys model (the state dict) and step. Other contents:
tokenizer/: bn_bpe32k.model and bn_bpe32k.vocab.results/: per-seed perplexity and probe accuracy, plus seed-1 per-pair outputs.In results/, s1 means seed 1337 and s2 means seed 2024.
Loading a checkpoint needs the code from
github.com/sahilaf/Mamba3_Bangla_Case_Study.
Mamba-3 and the hybrid also need a CUDA GPU and mamba-ssm>=2.3.2.
python -m bangla_ssm.eval_minimal_pairs \
--config transformer_seed1337/config.json --ckpt transformer_seed1337/checkpoint.pt \
--sp_model tokenizer/bn_bpe32k.model --tsv sva.tsv --out results.json
The probe TSVs are in sahilfarib/bangla-agreement-probes.
@inproceedings{farib2026statetracking,
title = {State Tracking vs. Attention in a Morphologically Rich Low-Resource Language:
A Controlled {B}angla Case Study with {M}amba-3},
author = {Sahil Al Farib and Momota Ahsana Meem and Sheikh Redwanul Islam and
Khan Md Shams Arefin and Azizur Rahman Anik},
booktitle = {Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)},
year = {2026},
note = {To appear}
}
The models are trained on FineWeb-2 (ODC-By 1.0) and released under the MIT license.