CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG
Optimizing Embedding Models with Optimum Intel and IPEX
Optimizing embedding models with Optimum Intel and IPEX involves accelerating models on Intel Hardware using techniques like low-bit quantization, model weight pruning, and distillation. The process takes advantage of Intel AVX-512, VNNI, and Intel AMX to accelerate models, including BFloat16 and int8 GEMM accelerators. The post-training static quantization method involves calibrating weights and activations, quantizing the model to minimize accuracy loss. Loading and running inference with a quantized model are also demonstrated, showing how to encode sentences into vectors. The evaluation involves comparing quantized models to original models using MTEB tasks like retrieval and reranking, with quantized models showing less than 1% error rate compared to original models.
Optimized Embedding Models with fastRAG
In this section, we explore how to integrate optimized Retrieval/Reranking models into fastRAG for efficient and optimized retrieval augmented generative pipelines. We discuss the example use of QuantizedBiEncoderRetriever and QuantizedBiEncoderRanker modules, highlighting the process of fast indexing using the optimized Retriever and reranking using the Optimized Ranker. Code examples are provided for creating a dense index, adding and encoding documents, as well as loading and utilizing the optimized model in a pipeline. The section concludes with an invitation to explore more RAG-related methods, models, and examples in the fastRAG repository.
FAQ
Q: What techniques are used for optimizing embedding models with Optimum Intel and IPEX?
A: Techniques like low-bit quantization, model weight pruning, and distillation are used for optimizing embedding models with Optimum Intel and IPEX.
Q: How does the post-training static quantization method work in optimizing models with Optimum Intel and IPEX?
A: The post-training static quantization method involves calibrating weights and activations, quantizing the model to minimize accuracy loss.
Q: What hardware features of Intel are utilized to accelerate models in the optimization process?
A: Intel AVX-512, VNNI, and Intel AMX are utilized to accelerate models, including BFloat16 and int8 GEMM accelerators.
Q: What is the objective when comparing quantized models to original models during evaluation?
A: The objective is to show that quantized models have less than a 1% error rate compared to original models, especially in tasks like retrieval and reranking.
Q: What modules are highlighted in the integration of optimized Retrieval/Reranking models into fastRAG?
A: QuantizedBiEncoderRetriever and QuantizedBiEncoderRanker modules are highlighted in the integration of optimized models into fastRAG.
Q: What is the process involved in fast indexing and reranking using the optimized Retriever and Ranker in fastRAG?
A: The process involves creating a dense index, adding and encoding documents, as well as loading and utilizing the optimized model in a pipeline.
Q: Where can one explore more RAG-related methods, models, and examples according to the essai?
A: One can explore more RAG-related methods, models, and examples in the fastRAG repository as mentioned in the essai.
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