World Wire

In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. We configure the GPU environment, install the required dependencies, load the 3B-parameter vision-language model with automatic selection of bfloat16 or float16, and generate structured sample documents for testing. We then evaluate both the tiled Gundam inference mode and the faster Base mode for single-page OCR before extending the pipeline to multi-page PDF parsing with PyMuPDF and infer_multi(). Throughout the workflow, we preserve long-context generation settings, repetition controls, and structured output handling to process dense layouts, tables, paragraphs, and cross-page content in a reproducible end-to-end pipeline. Copy Code Copied Use a different Browser import subprocess, sys def pip_install(*pkgs): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs]) print(">> Ins...

Open speech recognition stopped being a Whisper monoculture some time in the last twelve months. In March 2026 Cohere released Transcribe , a 2B Apache 2.0 model that took the top of the Hugging Face Open ASR Leaderboard at 5.42% average word error rate. Five weeks later IBM shipped Granite Speech 4.1 2B at 5.33%. Since then ARK-ASR-3B and MOSS-Transcribe-preview-2B have posted lower numbers still. The top of that leaderboard is now separated by less than one WER point. That has a specific consequence for anyone choosing a model: rank is no longer the deciding variable. License, language coverage, streaming support, and cost per audio-hour are. This roundup compares the field on all four. First, a problem with the leaderboard number everyone quotes The Open ASR Leaderboard average is not a single fixed quantity, and the models currently listed side by side were not all scored the same way. Cohere’s 5.42% is an average across eight English test sets, ...

Tokenization is the one part of the language modeling stack that almost nobody profiles. Gigatoken , released by Marcel Rød (a PhD student from Stanford)  under an MIT license, argues that this was a mistake. The library encodes text at gigabytes per second on a single machine, against baselines that are already multithreaded Rust. The GPT-2 tokenizer benchmarking yields remarkable results: evaluated on the 11.9 GB owt_train.txt corpus using a 144-core AMD EPYC 9565 dual-socket setup, Gigatoken processes data at a staggering 24.53 GB/s . In comparison, OpenAI’s tiktoken achieves 36.0 MB/s, while HuggingFace tokenizers registers at 24.8 MB/s on the identical hardware configuration. These marks demonstrate performance advantages of 681x and 989x, respectively. On an Apple M4 Max with 16 cores, the same GPT-2 workload runs at 8.79 GB/s, or 1,268x HuggingFace tokenizers and 140x tiktoken. On a consumer AMD Ryzen 7 9800X3D, it runs at 6.27 GB/s, or 106x and 68x. The sp...