Hybrid Mamba-2 + Transformer 2.94B LLM (Nemotron-H style) — Korean 3B model pretrained from scratch on 7× NVIDIA B200 GPUs with SFT + DPO alignment
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Updated
Mar 26, 2026 - Python
Hybrid Mamba-2 + Transformer 2.94B LLM (Nemotron-H style) — Korean 3B model pretrained from scratch on 7× NVIDIA B200 GPUs with SFT + DPO alignment
Hybrid SSM-Attention language model on Apple Silicon with MLX — interleaving Mamba-2 and Transformer for efficient inference
Mamba-2-130M encrypted inference research with CKKS on DGX Spark. Python reference, native GPU kernel, and reproducible experiments.
Helix-v2 1B bilingual hybrid LLM - Nvidia
Run NVIDIA's Nemotron-Labs-TwoTower-30B-A3B block-wise diffusion LM natively on Apple Silicon with MLX — AR tower + full two-tower mask-diffusion, 4/6/8-bit + bf16.
A 390M-parameter Mamba2 + Differential Attention hybrid language model
VerDee: Vertical Deep Network on Mamba-2 — staged LoRA (shallow/mid/deep), early-exit routing, and domain experts. Pilot on 370M; 16GB GPU friendly. Research/experiment repo.
Build Your Own Mamba — From Math to Metal
GLACIER: Mamba with indexless memory. This project integrates the Mamba SSM with ICE-Lite, a virtual memory engine, to solve context rot. By adding persistent, time-aware memory, GLACIER gives Mamba the long-term recall of a Transformer while retaining its $O(N)$ speed. Apache 2.0 licensed, by Dopove.
Native AscendC Mamba2 selective scan / SSD forward-backward custom operator for Huawei Ascend 910B3 and 950PR, with CANN, torch_npu, A100 benchmarks and msprof profiling.
The training stack for PureByte: from a task definition to a verified GGUF specialist in 25 minutes to an hour on one consumer GPU. Synthetic look-alikes grounded in real bytes, leak checks against the exams, criteria frozen before training, multi-seed runs with controls, and proof that the C++ runtime gives the same answers.
Unofficial PyTorch reproduction for Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.
A simple, minimalistic, and explainable JAX implementation of Mamba 2 & Mamba 3
How much cheaper is it to inject a retrieved fact next to the answer than to make a Mamba-2 model recall it from context? A small, controlled study: inject vs long-context, 2-hop chains, and adjacency decay.
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