Best of PyTorch — December 2025

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·18w

   [Hands-on] Deploy and Run LLMs on your Phone!

   Fine-tune and deploy LLMs directly on iOS and Android devices using UnslothAI, TorchAO, and ExecuTorch. The tutorial walks through loading Qwen3-0.6B, preparing reasoning and chat datasets, training with quantization-aware methods, exporting to mobile-ready .pte format, and running the model locally on iPhone at ~25 tokens/second. The resulting model is ~470MB and runs 100% on-device without requiring cloud connectivity.

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2. 2
   Article

   Sebastian Raschka·21w

   From Random Forests to RLVR: A Short History of ML/AI Hello Worlds

   A chronological overview traces the evolution of beginner-friendly ML/AI examples from 2013 to 2025. Starting with Random Forests on Iris datasets and XGBoost on Kaggle competitions, it progresses through neural networks (MLPs, AlexNet), transformer models (DistilBERT, Llama 2 with LoRA), and culminates with reasoning models using RLVR on mathematical datasets. Each milestone reflects when methods became mainstream and accessible, often lagging years behind their initial publication due to tooling maturity and community adoption.

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3. 3
   Article

   Hugging Face·22w

   Transformers v5: Simple model definitions powering the AI ecosystem

   Hugging Face releases Transformers v5, marking five years since v4 with daily installs growing from 20,000 to 3 million. The library now supports over 400 model architectures and 750,000 community checkpoints. Version 5 focuses on simplicity through modular design, improved training support for both pre-training and fine-tuning, enhanced inference capabilities with continuous batching and a new serving API, and first-class quantization support. The release emphasizes interoperability across the ecosystem, enabling seamless integration with inference engines like vLLM and SGLang, local deployment tools like llama.cpp and MLX, and training frameworks like Unsloth and Axolotl.

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4. 4
   Article

   Hacker News·21w

   Tongyi-MAI/Z-Image

   Z-Image is a 6B parameter image generation model featuring three variants: Z-Image-Turbo (distilled for sub-second inference with 8 NFEs on H800 GPUs), Z-Image-Base (foundation model for fine-tuning), and Z-Image-Edit (specialized for image editing). Built on a Scalable Single-Stream DiT architecture, it excels at photorealistic generation, bilingual text rendering (English/Chinese), and instruction following. The model uses Decoupled-DMD distillation algorithm and DMDR (combining DMD with reinforcement learning) for few-step generation optimization. Available on Hugging Face and ModelScope with PyTorch and Diffusers support.

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