Stephen McConnachie (@mcnatch) header
Stephen McConnachie (@mcnatch) profile picture

Head of Data and Digital Preservation at the BFI National Archive. Working on digital preservation, audiovisual archiving, using AI with care. Opinions my own.

London, England
Joined November 2010
Stephen McConnachie retweeted
Tried this on a bunch of old newspapers. - 1760s Fraktur, polytonic Greek, pre-reform Russian: all read well to my untrained eye; layout included - Big broadsheets need full-res input: error rate 25% → under 10% (these are examples where many OCR models struggle.
Introducing Surya OCR 2.5! - Most accurate open OCR model under 1B params (85.9 on OmniParseBench) - 1.7x faster than Surya 2, 4.5 pages/s on H100 - 689M params, with character tokenization
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Stephen McConnachie retweeted
From recognizing text to understanding documents. 📄 Meet Xiaomi-OCR-0, an open-source document understanding model with just 0.8B parameters. It brings document parsing, key information extraction, and OCR-oriented visual question answering into one lightweight model. It scores 95.24 on Real5-OmniDocBench and 83.2 on average across five OCR-oriented VQA benchmarks. On OmniDocBench v1.6, Xiaomi-OCR-0 ranks first in document parsing performance among models with fewer than 1B parameters. Model weights, inference code, an interactive demo, MCP service, and Agent Skill are now open source. 👇 🤗 Huggingface: huggingface.co/xiaomi-resear… 🔗 GitHub: github.com/xiaomi-research/X… 📄 Demo: huggingface.co/spaces/loongw… #XiaomiAI #OCR #OpenSource
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Stephen McConnachie retweeted
running NVIDIA Nemotron 3.5 Transcribe (30B-A4B) on a single RTX 3060. 12GB VRAM, 16GB RAM. (config below) 90 seconds of three people talking over each other, done in 27s. speaker labels and word timestamps in one pass. 1 hour context. His Girl Friday (1940) is famous for the fastest overlapping dialogue ever filmed, so i threw it at it. stock NeMo-Speech.cpp can’t offload experts to cpu yet, so i added a small patch: n_cpu_moe (like llama.cpp --n-cpu-moe) plus a higher output budget, because the default cuts dense crosstalk off halfway. config: nemo-speech serve --asr-model NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-AD-IQ4_NL.gguf --asr.salm.n_cpu_moe 40 --asr.salm.max_sequences 1 --asr.salm.n_ctx_per_sequence 73728 --asr.salm.max_context 73728 --asr.salm.n_batch 1024 --asr.salm.encoder_max_batch 4 env: NEMO_SALM_LOAD_MODE=mmap NEMO_SALM_OUT_TPS=12
Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵
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Stephen McConnachie retweeted
Added a script to run this on @huggingface Jobs with one command (@vllm_project + NVIDIA's NeMo plugin, inside the job). It's very fast: 4.9 hours of 1940s radio transcribed in 2.5 min once the model is up. Whole job, setup included: $0.48. Speaker tags + word timestamps, here on Hitchcock's 1940 Suspense audition: [5:21] spk2: No, I'll go. [5:39] spk1: Is it not true that you let lodgings? huggingface.co/datasets/uv-s…
Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵
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Stephen McConnachie retweeted
NEW from me: Britain’s AI testing agency outguns US Britain wants to pour another £120 million — roughly $160 million — into a government lab that tests the world’s most powerful AI models, an infusion of cash that alone is more than 10 times the annual budget of its US counterpart. It contrasts sharply with the approach in the US, where federal efforts to evaluate AI are receiving far less cash and are shared by different parts of government. semafor.com/article/10/27/20…
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Stephen McConnachie retweeted
Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵
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Stephen McConnachie retweeted
Artificial Analysis v4.3.2 to v5, AI models re-ranking step by step.
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Stephen McConnachie retweeted
Anthropic: “We cannot underscore enough the impact that UK @AISecurityInst has had, and will continue to have, on global AI safety and security.”
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Stephen McConnachie retweeted
Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: github.com/OpenBMB/MiniCPM-O… 🤗 Hugging Face: MiniCPM-OCR 1.0:huggingface.co/openbmb/MiniC… MiniCPM-OCR Jev:huggingface.co/openbmb/MiniC… 👉 Try the Demo:platform.modelbest.ai/consol… Use the API:platform.modelbest.ai/consol…
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Stephen McConnachie retweeted
OCR is not solved! I asked an OCR model claiming SOTA on popular benchmarks to read one line from an 1881 contents page. It wrote 16,421 characters. Mostly dots.
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Stephen McConnachie retweeted
🚀Excited to announce Nanbeige4.3-33B! To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first. Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters: * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B. huggingface.co/Nanbeige/Nanb… huggingface.co/Nanbeige/Nanb… #LLM #Agent
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Stephen McConnachie retweeted
Seeing some bad takes on British Government AI funding: some notes. 1. The Government has announced a tranche of funding for a number of different AI-related organisations and initiatives. It comes to roughly £600m (excluding private sector input.) This has led to a series of partisan bad takes by people who don’t really follow this conversation. That is annoying because only I can have partisan bad takes. 2. Two common takes: “this is nothing, AI labs spend hundreds of millions every day/month etc! We can’t compete! Common British L” Or “Government is terrible at picking winners and investing, should be left to the private sector.” These takes reflect usually partisan pre-existing notions, neither actually understands what this money does or doesn’t do, and neither actually examines what the money is going towards either. Apples to oranges type stuff. 3. Let’s break down two of the more significant sets. The first is more funding for AISI (+£120m), and the second is for a new AI Biosecurity Lab (£100m, + around £200m from frontier companies). 4. AISI is the world’s best resourced institute doing this work and leading in its class. Don’t take my word for it - feel free to read MI5 Chief’s recent speech, or any number of expert statements from frontier companies. IT IS NOT a frontier AI company competitor. IT IS NOT designed to produce cutting edge LLMs or agents or consumer applications. It is, effectively, a research organisation staffed by clever people whose job is to analyse what frontier AI companies are working on and test their stuff. So if you’re measuring this announcement of investment and saying “it’s nothing compared to what AI firms spend”, you’re comparing totally different things. Instead, look how much spending Britain’s AISI gets relative to its international counterparts on getting access and insight into this frontier. 5. The UK’s AI Biosecurity Lab (AIBL) is even newer than AISI, and a world first. IT IS NOT a competitor to private sector biolabs. IT IS NOT an example of Government intervening and ‘picking a winner.’ Its job - modelled after AISI - is to “develop technical capabilities that help the UK better understand, prevent, detect and respond to biological threats.” It will do this by attracting best in class talent + working with frontier companies + working with the UK’s wider biosecurity landscape + other agencies. 6. Both of these organisations represent continuity in British Government thinking: ‘AI brings with it a whole bunch of positive things, but also risks. We should create organisations that attract top talent to help us analyse and understand this technology.’ You see this in MI5’s recently launched AI Threat Assessment Centre (AITAC) and the AI Economics Institute (AIEI) launched earlier this year. You can absolutely think this is the right or wrong approach btw but that’s the broad strategy. 7. So the comparisons to frontier companies’ spending is just misplaced. You’re working yourself into a tizzy over a misunderstanding of why the state is allocating resources. You shouldn’t compare these to frontier companies, but instead to other AISIs or other foreign govt AI investments I guess. Finally, if I asked many of those commenting what they would do differently, I would guess they wouldn’t really know.
Huge. British Govt announces: • £165m: National AI Biosecurity Initiative: Includes £100m for a new AI Biosecurity Lab, using AI to detect emerging biological threats. • £120m: AI Security Institute (AISI): Additional funding to strengthen AI risk research and evaluation. • £70m: National Exposure and Real-Time Vulnerability (NERV) Centre: Identifying vulnerabilities in critical infrastructure. • £60m: National Cyber Shield: Strengthening Britain’s cyber defences. • £60m: UK Sovereign AI: Expanding investment into physical and industrial AI. • £60m: R&D Procurement Scheme: Supporting companies developing AI security and NHS innovations. • £30m: Mayoral AI Partnerships: Helping local businesses adopt AI, create jobs and improve public services. • £23m : AI Economics Institute: Research into AI’s effects on productivity, employment and living standards. • £18m: UK Advanced Chip Design Academy: Developing domestic semiconductor design expertise and reducing overseas dependencies.
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Stephen McConnachie retweeted
Huge. British Govt announces: • £165m: National AI Biosecurity Initiative: Includes £100m for a new AI Biosecurity Lab, using AI to detect emerging biological threats. • £120m: AI Security Institute (AISI): Additional funding to strengthen AI risk research and evaluation. • £70m: National Exposure and Real-Time Vulnerability (NERV) Centre: Identifying vulnerabilities in critical infrastructure. • £60m: National Cyber Shield: Strengthening Britain’s cyber defences. • £60m: UK Sovereign AI: Expanding investment into physical and industrial AI. • £60m: R&D Procurement Scheme: Supporting companies developing AI security and NHS innovations. • £30m: Mayoral AI Partnerships: Helping local businesses adopt AI, create jobs and improve public services. • £23m : AI Economics Institute: Research into AI’s effects on productivity, employment and living standards. • £18m: UK Advanced Chip Design Academy: Developing domestic semiconductor design expertise and reducing overseas dependencies.
1/7 I have two simple tests for British AI, driven by our values. Is it safe? And is it ours to shape? 🇬🇧 Today, Chancellor @JohnHealey_MP is announcing a major, ambitious package of investment to strengthen Britain's defences against emerging threats, back British innovation, and build our technological independence.
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Stephen McConnachie retweeted
A bunch of new OCR models came out while I was on leave, so I ran some evals on 2,165 historical book pages. - @datalabto Chandra-OCR-2: 3rd of 20, close behind dots.ocr - @LightOnIO LightOnOCR-3-4B: 5th, and invents far less text on blank pages than LightOnOCR-2 - Indra OCR: runs on CPU and ties Tesseract
FineBooks wants to help re-OCR the public domain to unlock a rich source of data for AI, for researchers, and for enjoyment. Step one: work out which modern OCR models are actually good enough. So we built a leaderboard. 14 open models, scored on 2,165 pages of expert human transcription. The best read historical print at ~97.6% character accuracy, for under $2 per 1,000 pages. A new @huggingface and @AiEleuther project, with @storytracer! Blog post: huggingface.co/blog/finebook…
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Stephen McConnachie retweeted
This week, we launched Indra, a 1M parameter OCR model that's 15x faster than Tesseract on CPU and much more accurate. I still can't believe how good this model is.
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Stephen McConnachie retweeted
Introducing Indra, a 1M parameter OCR model: - 49 pages/s on CPU, 11x faster than PaddleOCR and 17x Tesseract - Beats them on OmniParseBench text (41.3 vs 35.8 and 19.3) - Full layout and line bounding boxes
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Stephen McConnachie retweeted
All my work online for a guaranteed 100 years. It's a new service being offered by WordPress. They tried it out on me. I think it's great. Explained here: wordpress.com/blog/2025/02/1…
This is such a gift to the world. All of @stewartbrand’s work: writing, festival shenanigans, & 50,000(!) photos, dating back to the 50s: stewartbrand.org/ (Where else could you find Marshall McLuhan, Brian Eno, and Marlon Brando in one place!?)
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Stephen McConnachie retweeted
World-leading document recognition. Beyond text extraction. Introducing LightOnOCR-3, our new family of state-of-the-art AI models for document intelligence. Building on LightOnOCR-2, it recognizes text and handwriting, describes images, extracts data from charts and preserves document structure, all in a single processing step. 🏆 Leading benchmark results : ahead of Chandra-OCR-2, Mistral OCR 4.1 and dots.mocr on OlmOCR-Bench, and first among open-weight models on ParseBench. 📊 More information from every document : turn text, tables, images and charts into content your business applications can use. ⚡Compact and efficient: 0.8B and 4B versions, with document processing up to twice as fast as LightOnOCR-2. 🔓Apache 2.0 license: use, adapt and deploy the models on your own infrastructure. From handwritten administrative files to financial reports and technical documents, make more of your information searchable and usable. 👉 Explore the models and benchmark results: huggingface.co/blog/lightona… 👉🏻Explore the model page on our Website : lighton.ai/research/lightono… #LightOn #DocumentIntelligence #OCR
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Stephen McConnachie retweeted
oh yes, finally a new ocr model! claiming sota while under 1b.
World-leading document recognition. Beyond text extraction. Introducing LightOnOCR-3, our new family of state-of-the-art AI models for document intelligence. Building on LightOnOCR-2, it recognizes text and handwriting, describes images, extracts data from charts and preserves document structure, all in a single processing step. 🏆 Leading benchmark results : ahead of Chandra-OCR-2, Mistral OCR 4.1 and dots.mocr on OlmOCR-Bench, and first among open-weight models on ParseBench. 📊 More information from every document : turn text, tables, images and charts into content your business applications can use. ⚡Compact and efficient: 0.8B and 4B versions, with document processing up to twice as fast as LightOnOCR-2. 🔓Apache 2.0 license: use, adapt and deploy the models on your own infrastructure. From handwritten administrative files to financial reports and technical documents, make more of your information searchable and usable. 👉 Explore the models and benchmark results: huggingface.co/blog/lightona… 👉🏻Explore the model page on our Website : lighton.ai/research/lightono… #LightOn #DocumentIntelligence #OCR
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Stephen McConnachie retweeted
Meet EmbeddingGemma 2, our first natively multimodal open model for on-device embeddings. It expands beyond text to unify code, images, audio, and video in a shared space. 🧵
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