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  • [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita

    [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.PythonDeepLearningTensorFlowPyTorchOpenVINO 日本語　English - Japanese - 1. Introduction 今回は私が半年間掛けてためてきた、学習済みモデルの量子化ワークフローをメモがてら共有したいと思います。 Tensorflow の checkpoint (.ckpt/.meta)、 FreezeGraph (.

      [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita

    • TF2.0のKerasでPost-training quantization

      以前、TF-2.0rc1でtf.kerasのMobileNet v2をfine-tuinginし、Post-training quantizationするノートブックを作った。 TF2.0がリリースされたので、このノートブックをもとにモデルを変換して、いろいろなTF-Lite model を比較してみようと思った。 TF2.0rc1でtf.kerasのMobileNet v2をfine-tuning、Post-training quantizationするnotebookを作ってみたので公開。 Google colabで実行可。 ・Weight quantization ・Float16 quantization ・Integer quantization ・Full integer quantization -> Edge TPU Modelhttps://t.co/18htw5SgFs

        TF2.0のKerasでPost-training quantization

      • Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

        LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. Our LLM.int8 blogpost showed how the techniques in the LLM.int8 paper were integrated in transformers using the bitsandbytes library. As we strive to make models even more accessible to anyone, we decided to collaborate with bitsandbytes again to allow users to run models

          Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

        • Quanto: a PyTorch quantization backend for Optimum

          Quantization is a technique to reduce the computational and memory costs of evaluating Deep Learning Models by representing their weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32). Reducing the number of bits means the resulting model requires less memory storage, which is crucial for deploying Large Language Models

            Quanto: a PyTorch quantization backend for Optimum

          • GitHub - bitsandbytes-foundation/bitsandbytes: Accessible large language models via k-bit quantization for PyTorch.

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              GitHub - bitsandbytes-foundation/bitsandbytes: Accessible large language models via k-bit quantization for PyTorch.

            • GitHub - microsoft/Olive: Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs.

              Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. Given a model and targeted hardware, Olive composes the best suitable optimization techniques to output the most efficient model(s) for inferring on cloud or edge, while taking a set of constraints such as accuracy and latency into consid

                GitHub - microsoft/Olive: Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs.

              • Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

                Improving scalability There are several ways to approach the challenges of scaling embeddings. The most common approach is dimensionality reduction, such as PCA. However, classic dimensionality reduction -- like PCA methods -- tends to perform poorly when used with embeddings. In recent news, Matryoshka Representation Learning (blogpost) (MRL) as used by OpenAI also allows for cheaper embeddings.

                  Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

                • GitHub - Lightning-AI/lit-llama: Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

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                    GitHub - Lightning-AI/lit-llama: Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

                  • Creating a 17 KB style transfer model with layer pruning and quantization - Fritz ai

                    Home » Blog » Creating a 17 KB style transfer model with layer pruning and quantization There are now a bunch of off-the-shelf tools for training artistic style transfer models and thousands of open source implementations. Most use a variation of the network architecture described by Johnson et al to perform fast, feed-forward stylization. As a result, the majority of the style transfer models you

                    • Practical Quantization in PyTorch

                      by Suraj Subramanian, Mark Saroufim, Jerry Zhang Quantization is a cheap and easy way to make your DNN run faster and with lower memory requirements. PyTorch offers a few different approaches to quantize your model. In this blog post, we’ll lay a (quick) foundation of quantization in deep learning, and then take a look at how each technique looks like in practice. Finally we’ll end with recommenda

                      • PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA

                        We are happy to announce PyTorch Lightning V1.2.0 is now publicly available. It is packed with new integrations for anticipated features such as: PyTorch autograd profilerDeepSpeed model parallelismPruningquantizationStochastic weights averaging+ more stability improvementsContinue reading to learn more about what’s available. As always, feel free to reach out on Slack or discussions for any quest

                          PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA

                        • GitHub - GaParmar/clean-fid: PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

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                            GitHub - GaParmar/clean-fid: PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
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