Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
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Sep 21, 2026 - Python
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Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
nlp kosmos deepnet multimodal bitnet pre-trained-model unilm minilm layoutlm beit document-ai layoutxlm trocr foundation-models llm beit-3 xlm-e mllm kosmos-1 textdiffuser
Official implementation for BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation (CoRL 2026, Spotlight)
Kindi 0.5: local LLM inference on low-cost NVIDIA GPUs (Kepler to the RTX 50 series and the CMP 170HX), AMD Radeon 780M, 840M and 8060S, and CPUs. Bonsai 2B-27B, AH-Nanbeige4.3-33B and experimental Nemotron, with an OpenAI-compatible server.
pascal amd cuda kepler ternary ampere rocm maxwell quadro bitnet tensor-parallelism local-llm llm-inference openai-compatible low-cost-gpu
A ternary, zero-heap tiny language model that runs inside a $2 microcontroller — bit-exact Python <-> C99 <-> Cortex-M3 (QEMU) parity. Apache-2.0.
machine-learning microcontroller embedded cortex-m esp32 stm32 qemu quantization language-model ternary bitnet edge-ai on-device-ai tinyml llm small-language-model
Running Microsoft's BitNet via Electron, React & Astro
electron benchmarking ai reactjs 1-bit bitnet perplexity inference-framework astrojs large-language-models llm 1-bit-quantization
BitNet-Stack - Run a small BitNet model on local machine with one Docker command, and chat in browser.
Running Microsoft's BitNet inference framework via FastAPI, Uvicorn and Docker.
python docker benchmarking inference 1-bit bitnet perplexity uvicorn fastapi multi-chat server-orchestration llm model-context-protocol
Foundational ternary-model inference & training library — CUDA + CPU, BitNet/TQ ternary kernels
Official implementation of BitMamba-2. A scalable 1.58-bit State Space Model (Mamba-2 + BitNet) trained from scratch on 150B tokens. Includes JAX training code and high-performance C++ inference engine.
Ultra-lightweight C++ inference engine for BitMamba-2 (1.58-bit SSM). Runs 1B models on consumer CPUs at 50+ tok/s using <700MB RAM. No heavy dependencies.
Software side of an LLM running inside a DRAM chip via charge-sharing PIM (BitNet b1.58 on DRAM-Bender silicon).
BitNet: Learning-Based-Bit-Depth-Expansion
Single-launch batch-1 decode engine for PrismML Bonsai 27B (ternary and 1-bit) on NVIDIA GPUs. 1.76x the vendor llama.cpp fork on H100, same outputs.
Rust bindings for bitnet.cpp based on llama-cpp-4
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