̷̨̨̛̛̲͎̣̞̘͈̯͚͂̈́̂̄̽̎́̽̔͑̄̏̽̏͒̾́̅̐̈́̾̎̆͆̽́͌̽̀̚̕̚̕͠͝͝
@Quantum_Stat
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҈̿ ̶̢̢̧̡̼̜̝̬͍̜̘̙͉̘͎͓͍̣̰͖̹͖͚̭̘̖̟͕̬̠̬͇̹̮͎̣̱̎̾͌̊́̇͛̅͂̀̀͑̃̈̀̓̏͌͌͋̐̾̒͋͋̏͋̽̈́͐͑̐̂͆̊̈́̾͌̓͌̕̚͝͝͠͠͝ͅ Repos: https://t.co/3x0DctKuld
Joined March 2018
🚀🚀 Super excited to share the latest benchmark results for our quantized BGE models. A few weeks ago, these models were introduced with the aim of enhancing performance and efficiency for generating embeddings. And we've now conducted thorough comparisons between running
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RT @rajkbnp: I love the #ChatGPT Cheat Sheet by Ricky Costa (@Quantum_Stat). which includes.🔹NLP Tasks.🔹Code.🔹Structured Output Styles.🔹Uns….
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Check the image below for an example of what I'm discussing 👇 We are soon releasing a notebook with an end-to-end example for anyone to replicate the compressed bge models which achieve great accuracy results on the MTEB Leaderboard.
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The .npz file is a dictionary, with keys mapping to input names in the ONNX spec and values as NumPy arrays filled with the data. - Keep all data samples in a directory, usually named "data.".
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📦 The NPZ files will be used by Sparsify to calibrate the ONNX model by using samples from a calibration dataset. 🔍 **Specifications**: - Each .npz file houses a single data sample, no batch dimension. This data sample takes a thrilling ride through the ONNX model. -.
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⚡Getting to Know the NPZ file format to Compress BGE Embedding Models ⚡. For One-Shot Quantization (INT8), Sparsify relies on the .npz format for data storage, a file format rooted in the mighty NumPy library.
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source library Sparsify! Not only is it ONNX and INT8 quantized (faster and lighter) but is able to run on CPUs using DeepSparse! 💥.cc @neuralmagic. Model:
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⚡IT HAPPENED!⚡. There's a new state-of-the-art sentence embeddings model for the semantic textual similarity task on Hugging Face's MTEB leaderboard 🤗!. Bge-large-en-v1.5-quant was the model I quantized in less than an hour using a single CLI command using Neural Magic's open
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Exciting News! 🚀 DeepSparse is now integrated with @LangChainAI , opening up a world of possibilities in Generative AI on CPUs. Langchain, known for its innovative design paradigms for large language model (LLM) applications, was often constrained by expensive APIs or cumbersome.
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🌟First, want to thank everyone for pushing this model past 1,000 downloads in only a few days!! Additionally, I added bge-base models to MTEB. Most importantly, code snippets were added for running inference in the model cards for everyone to try out!.
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lol.
Numbers 1-100 ranked, worst to best. 100. 39.99. 41.98. 8.97. 43.96. 59.95. 74.94. 61.93. 89.92. 58.91. 12.90. 14.89. 19.88. 38.87. 71.86. 73.85. 55.84. 56.83. 37.82. 45.81. 76.80. 78.79. 96.78. 98.77. 87.76. 6.75. 68.74. 79.73. 85.72. 34.71. 42.70. 25.69. 18.68. 5.67. 84.66. 83.
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🚀🚀 Explore Sparsify's One-Shot Experiment Guide!. Discover how to quickly optimize your models with post-training algorithms for a 3-5x speedup. Perfect for when you need to sparsify your model with limited time and improved inference speedups.🔥. **FYI, this is what I used to
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🚀🚀 Hey, check out our blog on @huggingface 🤗regarding running LLMs on CPUs!. The blog discusses how researchers at IST Austria & Neural Magic have cracked the code for fine-tuning large language models. The method, combining sparse fine-tuning and distillation-type losses,.
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🚀✨ Run CodeGen on CPUs with this detailed Colab notebook! 📝. Explore how to sparsify and perform Large Language Model (LLM) inference using Neural Magic's stack, featuring Salesforce/codegen-350M-mono as an example. Dive into these key steps:. 1️⃣ **Installation**: Quickly set
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