Tags: pytorch*

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  1. The article details “autoresearch,” a project by Karpathy where an AI agent autonomously experiments with training a small language model (nanochat) to improve its performance. The agent modifies the `train.py` file, trains for a fixed 5-minute period, and evaluates the results, repeating this process to iteratively refine the model. The project aims to demonstrate autonomous AI research, focusing on a simplified, single-GPU setup with a clear metric (validation bits per byte).

    * **Autonomous Research:** The core concept of AI-driven experimentation.
    * **nanochat:** The small language model used for training.
    * **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
    * **program.md:** The file containing instructions for the AI agent.
    * **Single-File Modification:** The agent only edits `train.py`.
  2. This repository provides the official implementation of the STATIC (Sparse Transition-Accelerated Trie Index for Constrained decoding) framework, as described in Su et al., 2026. STATIC is a high-performance method for enforcing outputs to stay within a prespecified set during autoregressive decoding from large language models, designed for maximum efficiency on modern hardware accelerators like GPUs and TPUs.
  3. This project guides you from data inspection and preprocessing to crafting an end-to-end application for aircraft localization based on crowdsourced air traffic control communication data (ADS-B). It utilizes Apache Spark, Modin, ensemble methods, TabNet, Apache Kafka, Flask, and Leaflet.js.
  4. Train your neural network in TensorFlow or PyTorch, and run it inside CircuitPython using a single line of Python code.
  5. Learn how to design, develop, deploy and iterate on production-grade ML applications.
  6. This is the code repository for Causal Inference and Discovery in Python, published by Packt. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more.
  7. oLLM is a Python library for running large-context Transformers on NVIDIA GPUs by offloading weights and KV-cache to SSDs. It supports models like Llama-3, GPT-OSS-20B, and Qwen3-Next-80B, enabling up to 100K tokens of context on 8-10 GB GPUs without quantization.
  8. The core mechanics of Deep Learning, and how to think the PyTorch way. This guide provides a whirlwind tour of PyTorch’s methodologies and design principles, covering tensors, automatic differentiation, and training custom neural networks.
  9. This tutorial introduces the essential topics of the PyTorch deep learning library in about one hour. It covers tensors, training neural networks, and training models on multiple GPUs.
  10. Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi, pretty much any device!

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