Intel® Extension for Transformers: Accelerating Transformer-Based Models Everywhere
Overview
Intel® Extension for Transformers is a cutting-edge toolkit designed to enhance the performance of Transformer-based models across various Intel platforms. This toolkit is tailored to accelerate Generative AI (GenAI) and Large Language Models (LLMs) by optimizing their operation on devices like Intel Gaudi2, Intel CPUs, and Intel GPUs.
Key Features
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Model Compression: The toolkit seamlessly integrates with Hugging Face transformers APIs, and utilizes the Intel® Neural Compressor to offer a user-friendly experience for compressing models without sacrificing performance.
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Advanced Optimization: By leveraging innovative software optimizations and a unique compression-aware runtime, this extension provides enhanced efficiency for Transformer-based models. These improvements are based on the latest research, including work presented at NeurIPS conferences.
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Optimized Model Packages: Intel's toolkit supports a range of popular models like Stable Diffusion, GPT-J-6B, and BLOOM-176B. It also presents workflows for applications such as text classification and sentiment analysis, ensuring versatile use across different AI tasks.
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NeuralChat Framework: Create your personalized chatbots quickly with a flexible framework that includes numerous plugins for features like knowledge retrieval and speech interaction. This framework supports operations on Intel Gaudi2, CPU, and GPU platforms.
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Efficient Inference: Conduct inference on large language models using C/C++ for precise quantization, which is optimized for Intel CPUs and GPUs. This feature supports many leading models, enhancing the speed and performance of AI deployments.
Recent Developments
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Support for Qwen2 and Meta Llama 3: The toolkit now supports these advanced models, with detailed guides and blogs for implementation.
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Intel Meteor Lake and Xeon Improvements: With new INT4 inference support and significant performance boosts in GPT-J inference, the extension leverages the capabilities of modern Intel hardware.
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Expanded Chatbot Demonstrations: With new releases like NeuralChat-v3-1 and a 4-bit chatbot demo, users can experience improved communication capabilities.
Installation and Setup
Installing the Intel® Extension for Transformers is straightforward. It can be quickly set up via PyPI with the command:
pip install intel-extension-for-transformers
For detailed system requirements and installation instructions, one can refer to the Installation Guide provided by Intel.
Supported Platforms
The toolkit supports fine-tuning and inference on various Intel processors, including:
- Intel Gaudi2
- Intel Xeon Scalable Processors
- Intel Xeon CPU Max Series
- Intel Data Center GPU Max Series
- Intel Arc A-Series
Supported Software
Works seamlessly with popular software like:
- PyTorch
- Intel® Extension for PyTorch
- Transformers
- Synapse AI
Operating Systems
Validated on Ubuntu 20.04/22.04 and CentOS 8, ensuring broad compatibility for deploying AI solutions.
Getting Started
Creating a Chatbot: Users can quickly set up a chatbot using a simple Python script. The framework supports both RESTful API interactions and offline chatbot creation for flexible application.
Low-Bit Inference: The toolkit provides capabilities for INT4 inference on both CPUs and GPUs, enhancing the efficiency of models running on Intel hardware.
Extending Transformers and Langchain APIs: With sample code provided, users can implement extended APIs in their applications for tasks such as language model inference or retrieval-based QA systems.
Validated Models
A comprehensive list of validated models, along with their performance metrics, is available for users seeking to leverage pre-validated AI models in their projects.
Intel® Extension for Transformers represents a significant leap forward in making advanced AI technologies faster and more accessible on Intel hardware. Its user-friendly interfaces and cutting-edge optimizations make it an invaluable tool for anyone looking to harness the power of Transformer-based models.