Mar 14 · 17 min read · TLDR: There is no single "best" LLM quantization. You classify and choose quantization along three axes: when you quantize (timing), what you quantize (scope), and how values are encoded (mapping). In practice, most teams start with weight quantizati...
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Mar 8 · 13 min read · TLDR: Quantization converts high-precision model weights and activations (FP16/FP32) into lower-precision formats (INT8 or INT4) so LLMs run with less memory, lower latency, and lower cost. The key is choosing the right quantization method for your a...
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Jul 23, 2025 · 3 min read · I’m an AI engineer with extensive experience in model development, optimization, and deployment. My passion lies in building intuitive and efficient ecosystems and platforms for developers. I believe that the future of AI lies in the seamless integra...
Join discussionFeb 5, 2025 · 3 min read · Custom Training Loop Custom Training Loops: These provide more control over the training process compared to the standard Keras fit method. You can tailor the training to specific needs, such as implementing complex strategies or custom loss function...
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Oct 26, 2024 · 5 min read · When it comes to evaluating machine learning models, two key concepts stand out: residuals and cost functions. These terms play a crucial role in determining how well our model predicts outcomes. In this blog post, we will explore these concepts in d...
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Jul 29, 2024 · 6 min read · Have you ever wondered what it would be like to have a supercharged AI model that fits in your pocket? Imagine running complex machine learning algorithms on your smartphone without draining the battery or causing it to overheat. Or, imagine doubling...
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