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<p class="blog-date">December 20, 2024</p>
<h4>
<a href="/blog/improve-rag-performance/">Improve RAG performance with torch.compile on AWS Graviton Processors</a>
</h4>
<p>Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to support tasks like answering questions, translating languages, and completing sentences. There are a few challenges when working with LLMs such as domain knowledge gaps, factuality issues, and hallucination, which affect their reliability especially for the fields that require high levels of accuracy, such as healthcare, law, or engineering. Retrieval Augmented Generation (RAG) provides a soluti...</p>
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<a href="/blog/torchcodec/">torchcodec: Easy and Efficient Video Decoding for PyTorch</a>
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<p>We are pleased to officially announce torchcodec, a library for decoding videos into PyTorch tensors. It is fast, accurate, and easy to use. When running PyTorch models on videos, torchcodec is our recommended way to turn those videos into data your model can use.
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<p class="blog-date">December 06, 2024</p>
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<a href="/blog/accelerating-gemms-triton/">Accelerating 2D Dynamic Block Quantized Float8 GEMMs in Triton</a>
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<p>2D block quantization for Float8 (FP8) holds the promise of improving the accuracy of Float8 quantization while also accelerating GEMM’s for both inference and training. In this blog, we showcase advances using Triton for the two main phases involved in doing block quantized Float8 GEMMs.
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<p class="blog-date">December 02, 2024</p>
<h4>
<a href="/blog/hadacore/">HadaCore: Tensor Core Accelerated Hadamard Transform Kernel</a>
</h4>
<p>Quantization is a method for improving model inference speeds by compressing model weights and performing (faster) computation in lower precision data types. However, quantization can result in accuracy loss due to the presence of outliers.</p>
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<a href="/blog/training-using-float8-fsdp2/">Supercharging Training using float8 and FSDP2</a>
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<p>In this blog, we will demonstrate how we achieve up to 50% throughput speedup while achieving loss and evaluation benchmark parity in training over FSDP1 bf16 training</p>
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<h4>
<a href="/blog/rebellions/">Rebellions Joins the PyTorch Foundation as a General Member</a>
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<p>The PyTorch Foundation, a neutral home for the deep learning community to collaborate on the open source PyTorch framework and ecosystem, is announcing today that Rebellions has joined as a general member.</p>
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<a href="/blog/llama-into-torchtune/">Distilling Llama3.1 8B into 1B in torchtune</a>
</h4>
<p>In this blog, we present a case study on distilling a Llama 3.1 8B model into Llama 3.2 1B using torchtune’s knowledge distillation recipe. We demonstrate how knowledge distillation (KD) can be used in post-training to improve instruction-following task performance and showcase how users can leverage the recipe.
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