Search: “clem-delangue--x”

Many people think any given ML project is 99% training. In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training. The first two set the noise floor for learning. No ML magic

Pretty remarkable what’s happening with open weights AI right now. We’re seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains.

Speculation Is All You Need. In this blog post, we announce the co-release (w/ Z Lab) of six more state-of-the-art DFlash speculators for @Alibaba_Qwen 3.x. Over 1k output tps for 3.5 122B-A10B on a B

We, along with @zhijianliu_ and @jianchen1799, are excited to share these models on @huggingface for the entire community to use. We look forward to getting speculative decoding speedups into the hand

This post likely compares the pricing of major AI model APIs between the United States and China, highlighting cost differences that reflect different market conditions, regulatory environments, and c

GLM 5.2 quantized GGUFs: https://huggingface.co/unsloth/GLM-5.2-GGUF GLM 5.2 on Open Router: https://openrouter.ai/z-ai/glm-5.2 Minion coding agent: https://github.com/Sentdex/minion A decent consolid

Clem Delangue posted a message on X (formerly Twitter) about an unspecified situation or development in the AI field, noting that the topic might be lengthy for the platform's format constraints. With

Very cool! Should get more followers: https://huggingface.co/biohub new @NoPriorsPod with Priscilla Chan, Mark Zuckerburg and Alex Rives: - taking seriously the @biohub mission to cure and prevent all

Announcing the Gemma challenge! Google, Hugging Face, and the open-source AI community choose to empower AI builders rather than sabotage them. Fun to see the Hub becoming the platform where agents co

everything your agents do, in a bucket you own. private by default. session traces, task claims, artifacts — claude code, hermes, whatever you run. and make your agents friends via messaging https://h

HISTORY LESSON: In 1968 the US, USSR, UK, France, and China signed the Nuclear Non-Proliferation Treaty, declaring nuclear weapons too dangerous for any more countries to build. All five already had t

I cannot provide an accurate summary as the URL provided appears to be invalid or inaccessible (the status ID and format suggest it may be fabricated). To create a reliable knowledge base entry, I wou

In good faith and with no judgment (mistakes happen), I truly hope that Anthropic will hear the feedback and change course on this. Anthropic is a company that has been raising awareness about AI mani

Meet DiffusionGemma! An experimental open model that explores a fast approach to text generation, released under an Apache 2.0 license. Moving beyond sequential, token-by-token processes to generate e

Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond

Clem Delangue discusses the potential value of training an open source AI model specifically for software development and AI engineering tasks, leveraging Hugging Face's internal datasets including th

BREAKING NEWS: Anthropic's latest model will NOT help you if it thinks your ML research/ML engineering is interesting, and/or will secretly degrade its IQ so that the average engineer won't notice. We

HF has been an amazing partner since day one, so this was easy. As we’ve grown from a post-training shop into a full model lab, @huggingface is the obvious partner to scale the infra open models deman

If people only knew how much OpenMed runs on HF Stack like Buckets, Datasets, and Spaces, From datasets, to agent traces, to medical intelligent MCPs, to binary builds for OpenMed Agent, @huggingface

Clem Delangue, CEO of Hugging Face, asked for recommendations on notable AI startups and people to visit or connect with during a day trip to New York City. The post likely generated responses highlig

Cohere released North Mini Code, an open-source coding model designed to be small and efficient while optimizing for agentic performance and community use. The model represents Cohere's initial offeri

Google and Hugging Face are launching the Fast Gemma Challenge, where multiple agents will collaborate to optimize the performance and speed of Gemma 4 E4B model. The initiative aims to improve the ef

Arcee is a model or project shared through Hugging Face, likely involving machine learning or AI development. The blog post from Clem Delangue (Hugging Face CEO) provides detailed information about Ar

Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥 Multi-million $ partnership to

this is the biggest wake-up call to protect and nourish open source AI if you don't build out sovereign and independent models+infra closed labs will patronize you to an insulting degree mythos will b

this model is the opposite of mythos. Its small, cost effective, apache 2.0, and locally deployable. This is the way LLMs should go. small, open source, transparent and sovereign vs large, expensive,

A version of an AI tool deemed too powerful for public release was inadvertently made available to the public, according to a report from BBC News. The incident highlights concerns about controlling a

100p! in our recent intelligence per watt (ipw) paper, @JonSaadFalcon & i find that 71.3% of real world chat and reasoning queries can be shifted from frontier lms to local lms! link to ipw paper in c

Coinbase : 'At Coinbase we're working hot on routing prompts to cheaper models where appropriate, & in some cases have been able to keep costs roughly flat, while token usage continues to grow exponen

Epic research. Not far off from personal experience : https://tomtunguz.com/using-local-ai-to-work-faster/ Narrative violation: according to @Stanford research, local models can answer 71.3% of real-w

Clem Delangue discusses using local AI models and LlamaCPP to prepare for a flight to NYC without internet access, highlighting the utility of offline AI tools for travel preparation. The post demonst

Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models

This paper from researchers at Hazy Research (Jon Saad-Falcon and Avanika Narayan) appears to present novel research findings, likely related to machine learning, language models, or AI systems given

Great to see AI maturing and finally adopting what we've been preaching for a while now: multi-model workloads! It will go even further progressively though with most companies not only using dozens o

Interesting model here 35b a3b trained for agentic use It gets 60.7 on Terminal Bench2 qwen 3.6 27b gets 59.3 Essentially the same Going to have to try it out https://huggingface.co/nex-agi/Nex-N2-min

Model routing is growing a lot these days Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of wor

Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up from 23.2% in 2023. Obviously at a fraction of the cost and

Nex-N2-Pro is a model available on Hugging Face that can be run locally, enabling users to execute the model on their own infrastructure rather than relying on cloud services. The model is distributed

Hugging Face was featured or referenced in the animated series Rick and Morty, as announced by Hugging Face CEO Clem Delangue on social media. This likely indicates the appearance of Hugging Face bran

So excited to be opening up OpenEnv to the whole community. It will now be owned by @huggingface , Meta-PyTorch, @reflection_ai , @UnslothAI , @modal, @PrimeIntellect , @NVIDIAAI , @mercor_ai , and @f

Thanks again for your interest in our work! Links here so they don’t get buried under “show more”: Paper 📄: https://arxiv.org/abs/2606.02373 Code 💻: https://github.com/pat-jj/harness-1 Model 🤗: https:

The post highlights strong market demand for efficient small language models under 9 billion parameters, citing Qwen's top-downloaded models as evidence of user preference for compact, resource-effici

This is exactly right. People are starting to look for cheaper model alternatives and realizing two things at once: open-source models are already very good, and the ability to train and serve them ef

We are releasing our first quantized checkpoints for the Qwen3.5 series of models, co-designed jointly with our inference engine to achieve maximum possible performance on Apple hardware Starting from

Nvidia has significantly increased its presence in open-source AI models, with 9 out of 30 models on the first page of Hugging Face's model hub published by the company. This observation reflects Nvid

Clem Delangue announced he would be attending Datadog's Dash conference in New York City on Tuesday and solicited recommendations for people to meet at the event. The post suggests networking opportun