Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
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Updated
May 13, 2019 - Python
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
Flux diffusion model implementation using quantized fp8 matmul & remaining layers use faster half precision accumulate, which is ~2x faster on consumer devices.
JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Causal Parallel Tree Drafting
[ICML 2026] d3LLM: Ultra-Fast Diffusion LLM 🚀
Implementation of the paper Fast Inference from Transformers via Speculative Decoding, Leviathan et al. 2023.
[NeurIPS'23] Speculative Decoding with Big Little Decoder
🔥 Blazingly fast ML inference server powered by Rust and Burn framework
This is the official repo of "QuickLLaMA: Query-aware Inference Acceleration for Large Language Models"
Demo code for CVPR2023 paper "Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers"
AudioMuse-AI-DCLAP is a lightweight, high-speed distilled version of LAION CLAP, designed for fast and efficient text-to-music search
AI-powered legal assistant for Brazilian lawyers, built with Groq to deliver fast, accurate insights and document support.
An implementation of the encoder-decoder transformer for SMILES-to-SMILES translation tasks with inference accelerated by speculative decoding
Fast Forward-Only Deep Neural Network Library for the Nao Robots
Claude Code on Taalas's silicon-baked Llama 3.1 8B (~14,500 tok/s, ~1,000x faster than Opus). A tiny Anthropic-API proxy with tool translation, best-of-N reliability, and a safety guard. 🦞🏎️
Verification of the effect of speculative decoding in Japanese.
A self-contained CUDA inference engine for LiquidAI/LFM2.5-8B-A1B (hybrid conv + GQA-attention MoE, 8.5B params, 1B active) targeting a single RTX 3060 (12 GB). No Python, no frameworks at runtime: a single .cu engine + a header-only byte-level BPE tokenizer.
Reproducibility Project for [NeurIPS'23] Speculative Decoding with Big Little Decoder
Mamba-3 in a single file of pure PyTorch. Official mamba_ssm API, nothing to build, runs wherever PyTorch runs.
AudioMuse-AI-SAE implement the Sparse autoencoders (SAE) for DCLAP
Multilable fast inference classifiers (Ridge Regression and MLP) for NLPs with Sentence Embedder, K-Fold, Bootstrap and Boosting. NOTE: since the MLP (fully connected NN) Classifier was too heavy to be loaded, you can just compile it with the script.
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