SVDQuant: a new 4-bit post-training quantization paradigm for diffusion models
The rapid scaling of diffusion models has created challenges in memory usage and latency, making them difficult to implement, particularly ...
Tag: Quantization
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval
Neural contextual bias allows speech recognition models to leverage contextually relevant information, improving transcription accuracy. However, the bias mechanism is ...
GGUF Quantization with Imatrix and K-Quantization to run LLM on your CPU
Fast and accurate GGUF models for your CPUGenerated with DALL-EGGUF is a binary file format designed for efficient storage and ...
Eliminating Vector Quantization: Diffusion-Based Autoregressive AI Models for Image Generation
Autoregressive imaging models have traditionally relied on vector-quantized representations, which introduces several important challenges. The vector quantization process requires a ...
Quantization and LLM: condensing models to manageable sizes
The scale and complexity of LLMs The incredible capabilities of LLMs are powered by its vast neural networks that are ...
QoQ and QServe: A new frontier in model quantization transforming the implementation of large language models
Quantification, an integral method of computational linguistics, is essential for managing the vast computational demands of implementing large language models ...
Quantization, Linear Regression, and AI Hardware: Our Best Recent Deep Dives | by TDS Editors | April 2024
There are times when brevity is a blessing; Sometimes you just need to figure something out quickly to get on ...
KIVI – A Plug-and-Play 2-bit KV Cache Quantization Algorithm without the need for any tuning
Large language models (LLMs) are incredibly useful for tasks like generating text or answering questions. However, they face a big ...
HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce Computational and Memory Costs of Evaluating Deep Learning Models
HuggingFace researchers present How much to address the challenge of optimizing deep learning models for deployment on resource-constrained devices such ...
This article presents AQLM: a machine learning algorithm that helps in extreme compression of large language models using additive quantization
In the rapidly advancing domain of artificial intelligence, efficiently operating large language models (LLMs) on consumer-grade hardware represents a significant ...
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