---
title: "Python"
description: "252 posts about Python, summarised, each linking to the original."
---

# Python
> 252 posts about Python, summarised, each linking to the original.

## Articles

### [Introducing Waypoint-1: Real-time interactive video diffusion from Overworld](https://yomu.fyi/post/introducing-waypoint-1-real-time-interactive-video-diffusion-from-over.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Jan 20, 2026
- Type: Announcement

Overworld introduced Waypoint-1, a real-time interactive video diffusion model controllable through text prompts, mouse movements, and keyboard inputs. The architecture uses a frame-causal rectified flow transformer trained on compressed latent representations across 10,000 hours of video game footage paired with control signals. Pre-training uses diffusion forcing with causal attention, while post-training incorporates self forcing via DMD to mitigate autoregressive rollout error accumulation, enable one-pass classifier-free guidance, and support few-step denoising. To achieve low latency on consumer hardware, Overworld developed WorldEngine, a pure Python streaming inference library. Utilizing AdaLN feature caching, static rolling key-value caching with Flex Attention, fused matrix multiplications, and PyTorch compilation, the 2.3B parameter Waypoint-1-Small model reaches up to 60 frames per second on an RTX 5090 GPU.


### [NVIDIA brings agents to life with DGX Spark and Reachy Mini](https://yomu.fyi/post/nvidia-brings-agents-to-life-with-dgx-spark-and-reachy-mini.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jeff Boudier, Nader Khalil, Alec Fong
- Published: Jan 5, 2026
- Type: Tutorial

At CES 2026, NVIDIA presents a desk-based personal assistant that combines DGX Spark processing with a Reachy Mini robot, allowing an agent to converse, perceive camera input, speak, and perform actions. The tutorial assembles NVIDIA Nemotron 3 Nano for reasoning, NVIDIA Nemotron Nano 2 VL for vision, ElevenLabs for text-to-speech, and Reachy Mini hardware or simulation through the NeMo Agent Toolkit and Pipecat. Its configuration routes text, visual, and action requests to different models or a ReAct tool-calling agent, while the robot daemon exposes movement and state capabilities. The guide covers local, cloud, and serverless deployment, then starts the services needed for voice and vision interaction; the completed setup greets users and supports example text and camera prompts.


### [Tokenization in Transformers v5: Simpler, Clearer, and More Modular](https://yomu.fyi/post/tokenization-in-transformers-v5-simpler-clearer-and-more-modular.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ita Zaporozhets, Aritra Roy Gosthipaty, Arthur Zucker, Sergio Paniego, merve, Pedro Cuenca
- Published: Dec 18, 2025
- Type: Explainer

Transformers v5 overhauls its tokenization framework by separating tokenizer architecture from trained vocabularies. In contrast to v4's dual slow Python and fast Rust files, v5 consolidates each model tokenizer into a single file defaulting to the Rust-backed TokenizersBackend. The pipeline stages—normalizer, pre-tokenizer, model algorithm such as BPE or Unigram, post-processor, and decoder—are now directly exposed and configurable rather than buried in serialized files. Practitioners can instantiate blank tokenizer architectures and train custom vocabularies directly from iterators using native methods like train\_new\_from\_iterator while retaining model-specific formatting rules. The wrapper layer continues to bridge raw tokenization and model requirements by managing chat templates, context limits, and special token insertion.


### [From Python3.8 to Python3.10: Our Journey Through a Memory Leak](https://yomu.fyi/post/from-python3-8-to-python3-10-our-journey-through-a-memory-leak.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Jay Patel
- Published: Dec 15, 2025
- Type: Incident / postmortem

During an initiative to upgrade Python services from version 3.8 to 3.10, Lyft engineers encountered severe latency spikes and timeouts in one service within their test environment. Profiling DynamoDB queries revealed that gevent thread joins were taking up to 30 seconds while pod memory consumption climbed steadily. To isolate the issue, engineers used an internal tracemalloc-based profiler triggered via SIGUSR2 signals, temporarily disabling Gunicorn preload to prevent workers from terminating prematurely on signal receipt. Memory traces pointed to botocore and an incompatibility between weakref.finalize and gevent monkey patching in urllib3 version 1.26.16, which prevented connections from returning to the pool. Downgrading urllib3 to version 1.26.15 immediately resolved both the timeouts and the memory leak before a permanent fix arrived in gevent and urllib3 updates.


### [DeepMath: A lightweight math reasoning Agent with smolagents](https://yomu.fyi/post/deepmath-a-lightweight-math-reasoning-agent-with-smolagents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat
- Published: Dec 4, 2025
- Type: Problem & solution

Mathematical reasoning in large language models often suffers from lengthy chain-of-thought traces and frequent arithmetic mistakes. To address these issues, DeepMath pairs a Qwen3-4B Thinking base model with a sandboxed Python execution environment built using the smolagents library and vLLM backend. The system offloads deterministic calculations by emitting concise Python snippets, executing them safely with restricted module imports and no network access, and folding returned values back into the context. Training utilizes Group Relative Policy Optimization on the Tool-Integrated Reasoning subset of OpenMathReasoning with temperature scheduling and length constraints. Across benchmarks including MATH500, AIME, HMMT, and HLE, DeepMath reduces output token lengths by up to 66% while increasing overall problem-solving accuracy.


### [Transformers v5: Simple model definitions powering the AI ecosystem](https://yomu.fyi/post/transformers-v5-simple-model-definitions-powering-the-ai-ecosystem.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lysandre, Arthur Zucker, Cyril Vallez, Vaibhav Srivastav
- Published: Dec 1, 2025
- Type: Announcement

Transformers v5.0.0rc-0 introduces major architectural updates focused on simplicity, training, inference, and ecosystem interoperability across modern AI workflows. The release adopts a modular modeling approach and centralizes attention implementations into a unified AttentionInterface abstraction to reduce contribution and code review overhead. Support for Flax and TensorFlow is officially sunset in favor of focusing on PyTorch as the primary backend, while tokenization is standardized around the tokenizers library. For execution workloads, v5 adds native continuous batching, paged attention mechanisms, and a dedicated transformers serve OpenAI-compatible serving system. Finally, weight loading is refactored to make low-precision quantization a first-class citizen alongside broad interoperability with formats such as GGUF, MLX, and TorchAO.


### [Run for your money: Engineering a treadmill that prints store credit](https://yomu.fyi/post/run-for-your-money-engineering-a-treadmill-that-prints-store-credit.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Nov 25, 2025
- Type: Problem & solution

For the 2025 NYC marathon weekend, Shopify and Endorphins Running built a gamified treadmill that awarded in-store credit for maintaining a self-selected pace. Participants had 30 seconds to reach their target, then earned credit for each second spent within a defined pace zone, with a personalized barcode receipt redeemable through a Shopify POS UI extension. The implementation used a curved, motorless TrueForm Trainer, FTMS messages, a Python relay, Socket.IO, a local Remix application, receipt printing, and a production-hosted POS integration. Interval timestamps were normalized because treadmill messages arrived irregularly, while a state machine coordinated warm-up, gameplay, operator controls, database updates, discounts, and printing. The resulting experience attracted new customers, with 63% of participants identified as new customers, and its patterns could support other sensor-driven in-store incentives.


### [Diffusers welcomes FLUX-2](https://yomu.fyi/post/diffusers-welcomes-flux-2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: YiYi Xu, Daniel Gu, Sayak Paul, Alvaro Somoza, Dhruv Nair, Aritra Roy Gosthipaty, Linoy Tsaban, Apolinário from multimodal AI art
- Published: Nov 25, 2025
- Type: Tutorial

Diffusers introduces support for FLUX-2 models through the Flux2Pipeline and Flux2Transformer2DModel classes. Running the 4-bit quantized checkpoint diffusers/FLUX.2-dev-bnb-4bit requires loading Mistral3ForConditionalGeneration as the text encoder and the transformer in bfloat16 precision with CPU offloading enabled. During image generation with a prompt and 50 inference steps, the pipeline encodes text embeddings through the Mistral 3 model before executing diffusion operations. However, executing the pipeline on a GPU with 14.56 GiB capacity triggers a CUDA OutOfMemoryError during 4-bit dequantization inside bitsandbytes matrix multiplication operations. Additionally, the execution logs warn that Flax classes are deprecated in Diffusers and will be removed in version 1.0.0.


### [OVHcloud on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/ovhcloud-on-hugging-face-inference-providers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Gilles Closset, Fabien Ric, Elias Tourneux
- Published: Nov 24, 2025
- Type: Announcement

OVHcloud is now integrated as a supported Inference Provider on the Hugging Face Hub, expanding serverless inference options across model pages and client SDKs. The integration enables access to open-weight models, including gpt-oss, Qwen3, DeepSeek R1, and Llama, using European infrastructure with pay-per-token pricing starting at €0.04 per million tokens. Developers can connect via Python and JavaScript SDKs using either direct custom API keys or automatic routing through Hugging Face tokens. The service supports structured outputs, function calling, multimodal workflows, and embedding models while providing sub-200ms first-token response times. Calls routed through Hugging Face pass through standard provider pricing without markups, and PRO tier subscribers receive monthly inference credits.


### [SpellVault’s evolution: Beyond LLM apps, towards the agentic future](https://yomu.fyi/post/spellvault-s-evolution-beyond-llm-apps-towards-the-agentic-future.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Felix Haryanto Lie
- Published: Nov 21, 2025
- Type: Explainer

SpellVault began as Grab’s no-code platform for building LLM apps, using Retrieval-Augmented Generation (RAG) and plugins to connect applications with knowledge sources and external systems. Users could ground responses in Wikis, Google Docs, text, and PDFs, invoke custom HTTP endpoints, and access apps through the web, Slack, or APIs. The platform later added citations, Python execution for mathematical problems, scheduled actions, and drag-and-drop Workflows combining LLM calls, code, and Knowledge Vault lookups, while its executor moved toward graphs with nodes, edges, and states. SpellVault now exposes apps and RAG through MCP, allowing external agents and compatible clients to invoke apps and search, retrieve, or add Knowledge Vault information; TinyMCP adds MCP capabilities to FastAPI as a router.


### [20x Faster TRL Fine-tuning with RapidFire AI](https://yomu.fyi/post/20x-faster-trl-fine-tuning-with-rapidfire-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kamran Bigdely, Arun Kumar, Quentin Gallouédec
- Published: Nov 21, 2025
- Type: Announcement

Hugging Face TRL now officially integrates with RapidFire AI to accelerate LLM fine-tuning and post-training experimentation through concurrent configuration evaluation. Instead of executing training configurations sequentially, the platform shards datasets into discrete chunks and cycles multiple model configurations across GPUs at chunk boundaries. The runtime handles automatic checkpointing via a shared-memory-based adapter and model spilling mechanism to maintain consistent training state. Experimenters can monitor live metrics on an MLflow-based dashboard and use Interactive Control Ops to stop underperforming runs or clone and warm-start promising configurations mid-flight. Benchmark results on NVIDIA A100 GPUs demonstrate experimentation speedups ranging from 15x to 20x over sequential runs when training TinyLlama-1.1B and Llama-3.2-1B models.


### [Easily Build and Share ROCm Kernels with Hugging Face](https://yomu.fyi/post/easily-build-and-share-rocm-kernels-with-hugging-face.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abdennacer Badaoui, Daniel Huang, colorswind, Zesen Liu
- Published: Nov 17, 2025
- Type: Tutorial

Compiling custom deep learning GPU kernels across architectures often leads to complex build flag management, compiler errors, and ABI issues. Hugging Face's kernels library and kernel-builder streamline this workflow by supporting backends such as ROCm, CUDA, Metal, and XPU. Using the RadeonFlow FP8 block-wise GEMM kernel optimized for the AMD Instinct MI300X GPU as an example, developers organize files into specific headers and HIP implementations. The build is configured via a build.toml manifest and flake.nix to ensure reproducible compilation across supported PyTorch and ROCm versions. Artifacts can then be pushed to the Hugging Face Hub, allowing users to load and execute custom operators directly via get\_kernel without traditional installation steps.


### [Join the AMD Open Robotics Hackathon](https://yomu.fyi/post/join-the-amd-open-robotics-hackathon.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Eric Ma, Guruprasad MP
- Published: Nov 13, 2025
- Type: Announcement

AMD, Hugging Face, and Data Monsters announced the AMD Open Robotics Hackathon, an in-person competition scheduled for Tokyo and Paris in December 2025. Participating teams of up to four adults will compete over three days across two core missions. The first mission covers an instructor-led session on configuring the LeRobot development environment using AMD AI solutions. During the second mission, teams spend two days developing a freestyle project to address a real-world problem using LeRobot. Participants receive access to SO-101 robotics kits, AMD Ryzen AI laptops, and AMD Instinct MI300X GPUs via AMD Developer Cloud, with prizes awarded to the top seven teams in each city based on creativity, difficulty, ease-of-use, and practicality.


### [You Should Write An Agent](https://yomu.fyi/post/you-should-write-an-agent.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Nov 6, 2025
- Type: Tutorial

Thomas Ptacek argues that people should build an LLM agent before forming strong opinions about the technology, because implementation reveals how simple and surprising the underlying system can be. The example starts with a Python loop around the OpenAI Responses API, storing user and assistant messages in a context list to simulate conversation with a stateless model. Adding a JSON-defined ping function and a tool-calling loop lets the model choose and execute multiple network checks, demonstrating behavior not explicitly hard-coded in the loop. The discussion extends this pattern to coding agents, contexts persisted in SQLite, sub-agents, and design choices involving nondeterminism, ground truth, interchange formats, and token costs. It concludes that these remain open software-engineering problems whose practical behavior can be understood by building and iterating on agents.


### [Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac](https://yomu.fyi/post/building-a-healthcare-robot-from-simulation-to-deployment-with-nvidia.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steven Palma, Andres Diaz-Pinto
- Published: Oct 29, 2025
- Type: Tutorial

Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate into real-world physical systems. The NVIDIA Isaac for Healthcare v0.4 release introduces an end-to-end SO-ARM starter workflow to streamline simulation, training, and deployment for embodied surgical assistants. The architecture relies on a Sim2Real mixed training approach that fine-tunes the GR00T N1.5 foundation model on combined real-world and synthetic datasets using LeRobot. Synthetic data generated in simulation constitutes over 93 percent of the policy training data to bridge physical data collection constraints. Real-time inference on the SO-ARM101 precision manipulator utilizes RTI DDS communication, dual-camera vision, and TensorRT model conversion.


### [How to Build a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac for Healthcare](https://yomu.fyi/post/how-to-build-a-healthcare-robot-from-simulation-to-deployment-with-nvi.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Asawaree
- Published: Oct 28, 2025
- Type: Tutorial

Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate to physical hardware. NVIDIA introduced the Isaac for Healthcare v0.4 developer framework to streamline this lifecycle through an end-to-end SO-ARM starter workflow. Developers collect mixed real-world and synthetic datasets using LeRobot and SO-101 teleoperation, with synthetic generation accounting for over 93 percent of training data. The pipeline post-trains GR00T N1.5 models using dual-camera vision inputs, evaluates policies in Isaac Lab, and deploys them to physical hardware with RTI DDS communication. This integrated approach allows surgical assistant robots to interpret natural language instructions and execute physical manipulation tasks reliably.


### [Streaming datasets: 100x More Efficient](https://yomu.fyi/post/streaming-datasets-100x-more-efficient.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andres Marafioti, Quentin Lhoest, ben burtenshaw, Pedro Cuenca, merve
- Published: Oct 27, 2025
- Type: Problem & solution

Training machine learning models on multi-terabyte datasets often requires long download times or suffers from network request storms when multiple DataLoader workers initialize independently. Hugging Face revamped the backend of its datasets library to make dataset streaming viable for large-scale distributed training without modifying the standard API. Startup latency was reduced by caching resolved data file lists across workers and bundling API calls, cutting initial request volume up to one hundred times. The team also implemented background prefetching for Parquet files and added configurable buffering parameters to keep GPUs fully saturated during training runs. Benchmark tests demonstrated up to twice the sample processing throughput and zero worker crashes across 256 concurrent workers on 64 H100 GPUs.


### [huggingface\_hub v1.0: Five Years of Building the Foundation of Open Machine Learning](https://yomu.fyi/post/huggingface-hub-v1-0-five-years-of-building-the-foundation-of-open-mac.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lucain Pouget, Célina Hanouti, Lysandre, Julien Chaumond
- Published: Oct 27, 2025
- Type: Announcement

The Hugging Face Hub team released huggingface\_hub v1.0, marking the library's transition into a mature foundation for open machine learning after five years of development. The major update migrates the underlying network backend to httpx and replaces the legacy hf\_transfer tool with the hf\_xet binary package to enable chunk-level file deduplication across repositories. In addition, the release introduces a redesigned Typer-based CLI using the streamlined hf command alongside explicit authentication functions and expanded inference primitives. Legacy components, including the Git-based Repository class and the older InferenceApi interface, have been removed to establish a cleaner and more maintainable architecture. Most dependent machine learning libraries remain compatible across versions, though transformers requires v0.x for version 4 and v1.x for its upcoming version 5 release.


### [LeRobot v0.4.0: Supercharging OSS Robot Learning](https://yomu.fyi/post/lerobot-v0-4-0-supercharging-oss-robot-learning.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steven Palma, Michel Aractingi, Pepijn Kooijmans, Caroline Pascal, Jade Choghari, Francesco Capuano, Adil Zouitine, Martino Russi, Thomas Wolf
- Published: Oct 24, 2025
- Type: Announcement

LeRobot v0.4.0 expands the open-source robot-learning stack with scalable datasets, simulation benchmarks, processing tools, policies, and hardware integrations. LeRobotDataset v3.0 introduces chunked episodes, video streaming, unified Parquet metadata, improved loading and memory efficiency, plus utilities for editing and converting datasets. A modular Processor system separates policy and robot pipelines, while direct Accelerate integration simplifies multi-GPU training; the release also adds LIBERO, Meta-World, PI0, PI0.5, and GR00T N1.5 support. A plugin architecture enables third-party robots, cameras, and teleoperators to be installed without changing the core library, with Reachy 2 and phone teleoperation included, alongside a self-paced Robot Learning Course and hands-on tutorial.


### [Get your VLM running in 3 simple steps on Intel CPUs](https://yomu.fyi/post/get-your-vlm-running-in-3-simple-steps-on-intel-cpus.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ezequiel Lanza, Helena, Nikita, Ella Charlaix, Ilyas Moutawwakil
- Published: Oct 15, 2025
- Type: Tutorial

Deploying Vision Language Models locally presents computational challenges despite providing data privacy and independence from external servers. Optimum Intel and OpenVINO address this by optimizing lightweight models like SmolVLM2-256M for low-resource hardware, including Intel CPUs and GPUs. The process involves exporting the model to the OpenVINO Intermediate Representation format, applying 8-bit weight-only or static quantization, and executing inference with the transformed model. In single-image CPU benchmarks on an Intel Core Ultra 7 265K processor, OpenVINO with 8-bit weight-only quantization reduced time to first token from 5.150 seconds under PyTorch down to 0.247 seconds. The optimized configuration also raised decoding throughput from 0.722 to 63.928 tokens per second.


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