The model you need isn't on the Hub? Make it yourself! ML Intern turns a prompt into a trained, evaluated model on the Hub. It plans, asks for a budget, smoke-tests, trains, evaluates and publishes. 6 models trained in a weeks time at ~$100 compute. Link in reply👇

The model you need isn't on the Hub? Make it yourself! ML Intern turns a prompt into a trained, evaluated model on the Hub. It plans, asks for a budget, smoke-tests, trains, evaluates and publishes. 6 models trained in a weeks time at ~$100 compute. Link in reply👇

Kokoro TTS, but 10× smaller 🫰 Paradee distills Kokoro-82M into a 8M-param TTS just 9 MB of weights can speak faster than real time on a single CPU thread and runs on your toaster ▶️ on Spaces

🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward. However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally. In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity. Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.

4 Step, 260M text-to-image model ⌨️ Live typing, GPU: 🌐 Runs in your browser, nothing leaves your machine: 🧠 Model (MIT) Collection:

<190 millisecond for image generation on a T4. Image generated on every keystroke. ML-Intern distilled a 260M text-to-image model from 100 network passes per image to just 4. It runs on a small GPU Space, or right in your browser with WebGPU. Links in reply⬇️

4 Step, 260M text-to-image model ⌨️ Live typing, GPU: 🌐 Runs in your browser, nothing leaves your machine: 🧠 Model (MIT) Collection:

<190 millisecond for image generation on a T4. Image generated on every keystroke. ML-Intern distilled a 260M text-to-image model from 100 network passes per image to just 4. It runs on a small GPU Space, or right in your browser with WebGPU. Links in reply⬇️

Qwen-Image 2.1 draws its best pictures when a 9B "prompt rewriter" expands your request first. That is 20 GB in bf16 and uses 1700 words as system prompt, and thinks for ~1,600 tokens before answering. We shrank it to 0.8B. It fits on a laptop now. 🧵