Mamba2-Jax; Mamba2 implemented in pure Jax/Flax

Hacker News

I ported the Mamba2 state-space model (“Transformers are SSMs”) to pure JAX/Flax as mamba2-jax. – Core Mamba2 block with LM (Mamba2ForCausalLM) and time-series (Mamba2Forecaster) heads – Pure JAX/Flax (no Triton/custom CUDA), runs on CPU / CUDA / TPU via standard JAX backends – Small CPU-only parity test vs mamba2-torch: similar loss curves, final MSE diff ≈ 0.012, prediction correlation ≈ 0.99; after JIT warmup JAX was ≈ 2× faster per step I’d really appreciate feedback on: – API design, especially for streaming/stateful inference – Performance gotchas you hit if you try it – Any hooks you’d want exposed for research use PyPI: https://pypi.org/project/mamba2-jax/ Thanks, Cosmo

Visit product View on Hacker NewsNov 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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