Quantization-Aware Training
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin
arXiv:2606.09012v1 Announce Type: new Abstract: Post-training quantization (PTQ) converts a trained full-precision model into low-bit weights without task-level retraining, while quantization-aware training (QAT) incorporates quantization into the training loop. Although PTQ is efficient and often accurate at moderate bitwidths, it can fail sharply at aggressive bitwidths; QAT is more expensive but can often recover the lost accuracy. We propose a unified geometric framework that explains...
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
arXiv:2605.25054v2 Announce Type: replace Abstract: Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a leading compression approach; however, existing mixed-precision methods typically operate at coarse layer- or channel-level granularity. These methods often rely on heuristic or search-based bit-allocation strategies, which may overlook fine-grained variability...
WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization
arXiv:2605.26660v2 Announce Type: replace Abstract: Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime remains challenging. Existing post-training methods often suffer from severe accuracy degradation, while quantization-aware training requires costly retraining and additional resources.
Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency
Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency Since releasing Gemma 4 two months ago, we've been continuously working to expand its capabilities. First, we introduced Multi-Token Prediction (MTP) to accelerate inference, and just a couple of days ago, we released a 12B model to bridge the gap between our E4B and 26B MOE models. Today, we are releasing new checkpoints optimized with Quantization-Aware Training (QAT) to make Gemma 4 even more efficient, so...
Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillation
arXiv:2606.05682v2 Announce Type: replace Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization-aware distillation (QAD) helps recover accuracy lost under low bit quantization by training a quantized student to match the output distribution of a frozen higher precision teacher via a KL-divergence loss. In this work, we first provide a...
Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillatio
arXiv:2606.05682v1 Announce Type: new Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization-aware distillation (QAD) helps recover accuracy lost under low bit quantization by training a quantized student to match the output distribution of a frozen higher precision teacher via a KL-divergence loss. In this work, we first provide a...
On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
Announce Type: new Abstract: Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly understood. In this paper, we study uniform K-means quantization-aware training of ResNet-36 and ResNet-200 through joint layer-wise and score-level analyses.
Learning Quantized Continuous Controllers for Integer Hardware
arXiv:2511.07046v4 Announce Type: replace Abstract: Deploying continuous-control reinforcement learning policies on embedded hardware requires meeting tight latency and power budgets. Small FPGAs can deliver these, but only if costly floating-point pipelines are avoided. We study quantization-aware training (QAT) of policies for integer inference and we present a learning-to-hardware pipeline that automatically selects low-bit policies and synthesizes them to an Artix-7 FPGA.
Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
arXiv:2505.18113v2 Announce Type: replace Abstract: Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem. To tackle this challenge, the straight-through estimator (STE) has become the most widely adopted heuristic, allowing backpropagation through discrete operations by introducing biased yet valid surrogate gradients. However, its theoretical properties remain largely unexplored, with few existing analyses...
MiMo-v2.5-Pro-UltraSpeed: 1T model with 1000 tokens per second
From the first roaring racer of the combustion age to the sonic boom that shattered the sound barrier, humanity's hunger for speed is written into our very DNA. The speed of AI reasoning is no different — it defines the boundaries of intelligence itself. When a model is fast enough, it ceases to be a tool you wait on and becomes an extension of your own thinking: responding in real time, iterating in an instant, collaborating without friction.