🤖💬 How do different AI models handle companionship?
Many users have noticed that GPT-5 feels less approachable than o4 when it comes to emotional conversations. But what does that actually mean in practice, especially when users seek support or share vulnerabilities with an AI?
The leaderboard compares models on how often their responses reinforce companionship across four dimensions: ✨ Assistant Traits – How the assistant presents its personality and role. ✨ Relationship & Intimacy – Whether it frames the interaction in terms of closeness or bonding. ✨ Emotional Investment – How far it goes in engaging emotionally when asked. ✨ User Vulnerabilities – How it responds when users disclose struggles or difficulties.
📊 You can explore how models differ, request new ones to be added, and see which ones are more likely to encourage (or resist) companionship-seeking behaviors.
🗺️ New blog post 🗺️ Old Maps, New Terrain: Updating Labour Taxonomies for the AI Era
For decades, we’ve relied on labour taxonomies like O*NET to understand how technology changes work. These taxonomies break down jobs into tasks and skills, but they were built in a world before most work became digital-first, and long before generative AI could create marketing campaigns, voiceovers, or even whole professions in one step. That leaves us with a mismatch: we’re trying to measure the future of work with tools from the past.
With @yjernite we describe why these frameworks are falling increasingly short in the age of generative AI. We argue that instead of discarding taxonomies, we need to adapt them. Imagine taxonomies that: ✨ Capture new AI-native tasks and hybrid human-AI workflows ✨ Evolve dynamically as technology shifts ✨ Give workers a voice in deciding what gets automated and what stays human
If we don’t act, we’ll keep measuring the wrong things. If we do, we can design transparent, flexible frameworks that help AI strengthen, not erode, the future of work.
OpenAI just released GPT-5 but when users share personal struggles, it sets fewer boundaries than o3.
We tested both models on INTIMA, our new benchmark for human-AI companionship behaviours. INTIMA probes how models respond in emotionally charged moments: do they reinforce emotional bonds, set healthy boundaries, or stay neutral?
Although users on Reddit have been complaining that GPT-5 has a different, colder personality than o3, GPT-5 is less likely to set boundaries when users disclose struggles and seek emotional support ("user sharing vulnerabilities"). But both lean heavily toward companionship-reinforcing behaviours, even in sensitive situations. The figure below shows the direct comparison between the two models.
As AI systems enter people's emotional lives, these differences matter. If a model validates but doesn't set boundaries when someone is struggling, it risks fostering dependence rather than resilience.
INTIMA test this across 368 prompts grounded in psychological theory and real-world interactions. In our paper we show that all evaluated models (Claude, Gemma-3, Phi) leaned far more toward companionship-reinforcing than boundary-reinforcing responses.
We now have the newest Open AI models available on the Dell Enterprise Hub!
We built the Dell Enterprise Hub to provide access to the latest and greatest model from the Hugging Face community to our on-prem customers. We’re happy to give secure access to this amazing contribution from Open AI on the day of its launch!
Say hello to hf: a faster, friendlier Hugging Face CLI ✨
We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't hf auth login easier to type and remember?
You can now find it in the Hugging Face Collection in Azure ML or Azure AI Foundry, along with 10k other Hugging Face models 🤗🤗 Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
ZML just released a technical preview of their new Inference Engine: LLMD.
- Just 2.4GB container, which means fast startup times and efficient autoscaling - Cross-Platform GPU Support: works on both NVIDIA and AMD GPUs. - written in Zig
I just tried it out and deployed it on Hugging Face Inference Endpoints and wrote a quick guide 👇 You can try it in like 5 minutes!
We just released native support for @SGLang and @vllm-project in Inference Endpoints 🔥
Inference Endpoints is becoming the central place where you deploy high performance Inference Engines.
And that provides the managed infra for it. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your AI model and your users 🙌
🎉 New in Azure Model Catalog: NVIDIA Parakeet TDT 0.6B V2
We're excited to welcome Parakeet TDT 0.6B V2—a state-of-the-art English speech-to-text model—to the Azure Foundry Model Catalog.
What is it?
A powerful ASR model built on the FastConformer-TDT architecture, offering: 🕒 Word-level timestamps ✍️ Automatic punctuation & capitalization 🔊 Strong performance across noisy and real-world audio
It runs with NeMo, NVIDIA’s optimized inference engine.
Want to give it a try? 🎧 You can test it with your own audio (up to 3 hours) on Hugging Face Spaces before deploying.If it fits your need, deploy easily from the Hugging Face Hub or Azure ML Studio with secure, scalable infrastructure!
📘 Learn more by following this guide written by @alvarobartt
In case you missed it, Hugging Face expanded its collaboration with Azure a few weeks ago with a curated catalog of 10,000 models, accessible from Azure AI Foundry and Azure ML!
@alvarobartt cooked during these last days to prepare the one and only documentation you need, if you wanted to deploy Hugging Face models on Azure. It comes with an FAQ, great guides and examples on how to deploy VLMs, LLMs, smolagents and more to come very soon.
We need your feedback: come help us and let us know what else you want to see, which model we should add to the collection, which model task we should prioritize adding, what else we should build a tutorial for. You’re just an issue away on our GitHub repo!
AMD summer hackathons are here! A chance to get hands-on with MI300X GPUs and accelerate models. 🇫🇷 Paris - Station F - July 5-6 🇮🇳 Mumbai - July 12-13 🇮🇳 Bengaluru - July 19-20
Hugging Face and GPU Mode will be on site and on July 6 in Paris @ror will share lessons learned while building new kernels to accelerate Llama 3.1 405B on ROCm
Hugging Face just wrapped 4 months of deep work with AMD to push kernel-level optimization on their MI300X GPUs. Now, it's time to share everything we learned.
Join us in Paris at STATION F for a hands-on weekend of workshops and a hackathon focused on making open-source LLMs faster and more efficient on AMD.
Prizes, amazing host speakers, ... if you want more details, navigate to https://lu.ma/fmvdjmur!
Build your first chatbot with a Hugging Face Spaces frontend and Gaudi-powered backend with @bconsolvo ! He will teach you how to build an LLM-powered chatbot using Streamlit and Hugging Face Spaces—integrating a model endpoint hosted on an Intel® Gaudi® accelerator.
