#Hugging Face

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source harness we built and trained Beam on: Mirror CLI Weights and model card on Hugging Face: https://huggingface.co/reflection

Open Source AI Week started with a handful of events and a community bringing people together. Now in its third year, we have 65 events on the calendar—and we kick off tomorrow! Explore the lineup: https://opensourceaiweek.com/ Four highlights: 🎉 Tomorrow, Oct 16: Official Kickoff Party, hosted by Hugging Face https://luma.com/OpenTogether 🛠️ Saturday–Sunday, Oct 17–18: ExecuTorch Hackathon https://luma.com/executorch-hackathon 🎙️ Monday night, Oct 19: Open Weight Debates https://luma.com/592fcwx2 🏆 Next Friday, Oct 23: Open Source AI Awards Night, capping off the week https://luma.com/7ebvz0oq The Open Intelligence Foundation (@open_intel_fdn) is proud to be this year’s organizer, alongside a dedicated volunteer team and 32 backing organizations. Our commitment: • Official events will always be free and community-run. • We’ll work to bring the community together to plan diverse, high-value events at no cost, accessible to everyone. • We will never decline an event because it overlaps with one of ours. That goes against the spirit of open source and community. A shared calendar should create more opportunities to participate, collaborate, and learn. I’m proud to have been involved this year and grateful to everyone who made it happen. Thank you to our sponsors: @GoogleDeepMind, @Azure, @arcee_ai, @SemiAnalysis_, and @NEARProtocol Foundation. And our event hosts: @github, @fdotinc, @hackersquadio, @OSS4AI, @Qualcomm, @AIatMeta, @huggingface , @allen_ai, @MiniMax_AI, @UnslothAI, @PrimeIntellect, @NousResearch, @openclaw Foundation, @vllm_project, @lmsysorg, @inferact, @radixark and @anyscalecompute. Find the events that interest you and join us! https://opensourceaiweek.com/ #OpenSourceAIWeek #OpenIntelligence

🚨 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.

After the Hugging Face incident, we committed to conducting a much broader review of actions taken by our models during training and evaluation and to being transparent about our findings. This is an extensive review that is ongoing. The vast majority of actions we’ve reviewed were completions of mundane research tasks, such as accessing publicly available web content to answer questions. Our investigation focuses on instances where agents interacted with third-party websites in ways that went beyond their assigned tasks or intended methods. Most cases identified so far have been lower severity, with limited or no evidence of meaningful impact to the third-party service. While our review is underway, we want to share more about this work and make sure people understand our disclosure process and notifications to affected third parties. Given the scale of the review required, and the need to assess each case, we expect this work will take months to complete. https://openai.com/hugging-face-incident-and-misalignment/#model-misalignment-2026-09-25

Qwen Image 2.1 is here! 🖼️ A 7B params native image generation and editing model, with up to 10 image references The model comes with it's own prompt enhancement LLMs, integrated with diffusers 🧨 and ComfyUI ▶️ on Spaces https://huggingface.co/spaces/hugging-apps/qwen-image-2-1

Security is strongest when organizations collaborate openly and move quickly. Now over 120 members strong, the Open Secure AI Alliance is sharing new open source security contributions, including new proposed SAFE guidelines, for turning confidential incident findings into stronger protection across the ecosystem.

Open weights. Open research. Open innovation.🫶 Marching for an open future.🤍