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Github Quic Efficient Transformers This Library Empowers Users To

Leo Migdal
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github quic efficient transformers this library empowers users to

[04/2025] Support for SpD, multiprojection heads. Implemented post-attention hidden size projections to speculate tokens ahead of the base model [04/2025] QNN Compilation support for AutoModel classes. QNN compilation capabilities for multi-models, embedding models and causal models. [04/2025] Added support for separate prefill and decode compilation for encoder (vision) and language models. This feature will be utilized for disaggregated serving.

[04/2025] SwiftKV Support for both continuous and non-continuous batching execution in SwiftKV. [04/2025] Support for GGUF model execution (without quantized weights) QEfficient is a Python library designed for optimizing transformer models and deploying them efficiently on Qualcomm Cloud AI 100 hardware. This library provides a seamless path from pre-trained HuggingFace models to production-ready inference on specialized AI accelerators. The library abstracts the complexity of model transformation, ONNX export, hardware compilation, and inference execution while maintaining compatibility with the familiar HuggingFace transformers interface. It supports text generation models, embedding models, vision-language models, and speech-to-text models with advanced features like continuous batching, speculative decoding, and parameter-efficient fine-tuning.

For specific model architecture support, see Model Types and Architectures. For installation and environment setup, see Installation and Setup. For detailed API documentation, see Core Architecture. QEfficient follows a layered architecture that transforms standard transformer models through multiple stages to achieve optimal performance on Qualcomm AI 100 hardware: Sources: QEfficient/transformers/models/modeling_auto.py61-102 QEfficient/base/modeling_qeff.py39-80 README.md61-83 Reposting with immense pride: Our team has successfully designed, developed, and released a fantastic new library!

🎉 Huge thanks to everyone involved for their dedication and exceptional effort. Proud to be part of such a talented and driven team. Vinayak Narayan Baddi Onkar Chougule Mamta Singh ANN KURUVILLA Amit Raj Rishin Raj Here’s to many more achievements together! 🚀 Blog: https://lnkd.in/gKqUjD5H Library : https://lnkd.in/gcVhJqma #TeamWork #Innovation #AI #Qualcomm #genAI I'm thrilled to share the release of the efficient transformers library, which enables you to go from pretrained LLMs to inference-ready solutions with just a single API call. This powerful library enables developers to seamlessly port pretrained models and checkpoints from the Hugging Face hub into inference-ready formats optimized for Qualcomm Cloud AI 100 accelerators, enabling top performance with minimal effort.

Key features include: ✅A single-step process for exporting, compiling, and deploying models ✅Automated reimplementation of foundational model blocks optimized for Qualcomm Cloud AI 100 ✅Retain the ability to fine-tune, quantize, and adapt models ✅Compile... 👉 Check out the library: https://lnkd.in/gcVhJqma 🔗 Read the blog to learn more about the efficient transformers library and get a detailed walkthrough on deploying supported transformer-based models on Qualcomm Cloud AI 100 instances. https://lnkd.in/gKqUjD5H #ai #machinelearning #generativeai Assistant Professor, Computer Engineering , Sinhgad College of Engineering, Pune Director, Engineering at Qualcomm | AI Data Center Inference | Wireless Networks An open API service providing repository metadata for many open source software ecosystems.

This library empowers users to seamlessly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators. JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quic%2Fefficient-transformers PURL: pkg:github/quic/efficient-transformers License: other Language: Python Size: 99.8 MB Dependencies parsed at: Pending Created at: over 1 year ago Updated at: 4 days ago Pushed at: 4 days ago Last synced at: 4 days ago This library empowers users to seamlessly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators. Efficient Transformers Library provides reimplemented blocks of Large Language Models (LLMs) to make models functional and highly performant on Qualcomm Cloud AI 100.

It includes graph transformations, handling for under-flows and overflows, patcher modules, exporter module, sample applications, and unit test templates. The library supports seamless inference on pre-trained LLMs with documentation for model optimization and deployment. Contributions and suggestions are welcome, with a focus on testing changes for model support and common utilities. [04/2025] Support for SpD, multiprojection heads. Implemented post-attention hidden size projections to speculate tokens ahead of the base model [04/2025] QNN Compilation support for AutoModel classes.

QNN compilation capabilities for multi-models, embedding models and causal models. [04/2025] Added support for separate prefill and decode compilation for encoder (vision) and language models. This feature will be utilized for disaggregated serving. There was an error while loading. Please reload this page. QEfficient is a Python library that enables efficient deployment of transformer models on Qualcomm Cloud AI 100 hardware.

This document provides a comprehensive overview of the library's architecture, core components, and workflow for transforming HuggingFace models into optimized inference binaries. The library bridges the gap between pre-trained transformer models and hardware-accelerated inference by providing model wrappers, graph transformations, ONNX export capabilities, and compilation tools. For specific API usage patterns, see Getting Started. For detailed model architecture implementations, see Core Model System. For export and compilation workflows, see Model Export and Compilation. The QEfficient library follows a modular architecture that transforms models through multiple stages from HuggingFace format to Cloud AI 100 deployment.

Sources: QEfficient/__init__.py35-63 README.md55-63 pyproject.toml4-6 The library implements a standardized workflow for model optimization and deployment: [04/2025] Support for SpD, multiprojection heads. Implemented post-attention hidden size projections to speculate tokens ahead of the base model [04/2025] QNN Compilation support for AutoModel classes. QNN compilation capabilities for multi-models, embedding models and causal models.

[04/2025] Added support for separate prefill and decode compilation for encoder (vision) and language models. This feature will be utilized for disaggregated serving. [04/2025] SwiftKV Support for both continuous and non-continuous batching execution in SwiftKV. [04/2025] Support for GGUF model execution (without quantized weights)

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