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Tag: model weights

  • Simon Willison’s Weblog: Things we learned out about LLMs in 2024

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    Source URL: https://simonwillison.net/2024/Dec/31/llms-in-2024/#atom-everything Source: Simon Willison’s Weblog Title: Things we learned out about LLMs in 2024 Feedly Summary: A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying…

  • Simon Willison’s Weblog: DeepSeek_V3.pdf

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    Source URL: https://simonwillison.net/2024/Dec/26/deepseek-v3/#atom-everything Source: Simon Willison’s Weblog Title: DeepSeek_V3.pdf Feedly Summary: DeepSeek_V3.pdf The DeepSeek v3 paper (and model card) are out, after yesterday’s mysterious release of the undocumented model weights. Plenty of interesting details in here. The model pre-trained on 14.8 trillion “high-quality and diverse tokens" (not otherwise documented). Following this, we conduct post-training, including…

  • Simon Willison’s Weblog: Trying out QvQ – Qwen’s new visual reasoning model

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    Source URL: https://simonwillison.net/2024/Dec/24/qvq/#atom-everything Source: Simon Willison’s Weblog Title: Trying out QvQ – Qwen’s new visual reasoning model Feedly Summary: I thought we were done for major model releases in 2024, but apparently not: Alibaba’s Qwen team just dropped the Apache2 2 licensed QvQ-72B-Preview, “an experimental research model focusing on enhancing visual reasoning capabilities". Their blog…

  • Hacker News: Fast LLM Inference From Scratch (using CUDA)

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    Source URL: https://andrewkchan.dev/posts/yalm.html Source: Hacker News Title: Fast LLM Inference From Scratch (using CUDA) Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text provides a comprehensive overview of implementing a low-level LLM (Large Language Model) inference engine using C++ and CUDA. It details various optimization techniques to enhance inference performance on both CPU…

  • Cloud Blog: Orchestrating GPU-based distributed training workloads on AI Hypercomputer

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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/gpu-orchestration-options-on-ai-hypercomputer/ Source: Cloud Blog Title: Orchestrating GPU-based distributed training workloads on AI Hypercomputer Feedly Summary: When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs…

  • Hacker News: DSPy – Programming–not prompting–LMs

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    Source URL: https://dspy.ai/ Source: Hacker News Title: DSPy – Programming–not prompting–LMs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses DSPy, a framework designed for programming language models (LMs) rather than relying on simple prompting. It enables faster iterations in building modular AI systems while optimizing prompts and model weights, offering insights…

  • AWS News Blog: New Amazon EC2 P5en instances with NVIDIA H200 Tensor Core GPUs and EFAv3 networking

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    Source URL: https://aws.amazon.com/blogs/aws/new-amazon-ec2-p5en-instances-with-nvidia-h200-tensor-core-gpus-and-efav3-networking/ Source: AWS News Blog Title: New Amazon EC2 P5en instances with NVIDIA H200 Tensor Core GPUs and EFAv3 networking Feedly Summary: Amazon EC2 P5en instances deliver up to 3,200 Gbps network bandwidth with EFAv3 for accelerating deep learning, generative AI, and HPC workloads with unmatched efficiency. AI Summary and Description: Yes **Summary:**…

  • Simon Willison’s Weblog: Quantization matters

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    Source URL: https://simonwillison.net/2024/Nov/23/quantization-matters/#atom-everything Source: Simon Willison’s Weblog Title: Quantization matters Feedly Summary: Quantization matters What impact does quantization have on the performance of an LLM? been wondering about this for quite a while, now here are numbers from Paul Gauthier. He ran differently quantized versions of Qwen 2.5 32B Instruct through his Aider code editing…

  • Hacker News: Watermark Anything

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    Source URL: https://github.com/facebookresearch/watermark-anything Source: Hacker News Title: Watermark Anything Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses “Watermark Anything,” a method for embedding localized watermarks into images using pretrained models and a specific implementation within a Python environment. It outlines the installation process, utilization of the COCO dataset for training, and…

  • Cloud Blog: Data loading best practices for AI/ML inference on GKE

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    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke/ Source: Cloud Blog Title: Data loading best practices for AI/ML inference on GKE Feedly Summary: As AI models increase in sophistication, there’s increasingly large model data needed to serve them. Loading the models and weights along with necessary frameworks to serve them for inference can add seconds or even minutes of scaling…