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Siemens
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- https://richardburleigh.com.au
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Prompts for our Grok chat assistant and the `@grok` bot on X.
DFloat11: Lossless LLM Compression for Efficient GPU Inference
MAGI-1: Autoregressive Video Generation at Scale
colinurbs / FramePack-Studio
Forked from lllyasviel/FramePackAdding timestamped prompts and general quality of life features to FramePack https://discord.gg/MtuM7gFJ3V
Official inference framework for 1-bit LLMs
Official PyTorch implementation for Hogwild! Inference: Parallel LLM Generation with a Concurrent Attention Cache
Only implemented through torch: "bi - mamba2" , "vision- mamba2 -torch". support 1d/2d/3d/nd and support export by jit.script/onnx;
Simple go utility to download HuggingFace Models and Datasets
Command and Conquer: Generals - Zero Hour
Exploring an idea where one forgets about efficiency and carries out attention across each edge of the nodes (tokens)
Official PyTorch and Diffusers Implementation of "LinFusion: 1 GPU, 1 Minute, 16K Image"
This is InfiniRetri, a tool enhance Transformer-based LLMs(Large Language Model) ablity to hangle Long-Context.
Production-tested AI infrastructure tools for efficient AGI development and community-driven innovation
A Conversational Speech Generation Model
A high-performance distributed file system designed to address the challenges of AI training and inference workloads.
A lightweight data processing framework built on DuckDB and 3FS.
A bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling
DeepEP: an efficient expert-parallel communication library
FlashMLA: Efficient MLA decoding kernels
Implementation of the sparse attention pattern proposed by the Deepseek team in their "Native Sparse Attention" paper
Audiobook Creator is an open-source tool that converts books (EPUB, PDF, TXT) into fully voiced audiobooks with intelligent character voice attribution. It uses NLP, LLMs, and Kokoro TTS to generat…
Explorations into adversarial losses on top of autoregressive loss for language modeling