openai/gpt-oss-20b
Text Generation
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Brigitte Tousignant
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What is the Hugging Face Community Building?
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What is the Hugging Face Community Building?
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What Open-Source Developers Need to Know about the EU AI Act's Rules for GPAI Models
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What Open-Source Developers Need to Know about the EU AI Act's Rules for GPAI Models
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Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face
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Hugging Face just made life easier with the new hf CLI!
huggingface-cli to hf
With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.
To get started, just run:
pip install -U huggingface_hub
List of hf CLI Commands
Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.
Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.
Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.
hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
huggingface-cli to hf
With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.
To get started, just run:
pip install -U huggingface_hub
List of hf CLI Commands
Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.
Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.
Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.
hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
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sergiopaniego's
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We just released TRL v0.20 with major multimodal upgrades!
👁️ VLM support for GRPO (highly requested by the community!)
🎞️ New GSPO trainer (from @Qwen , released last week, VLM-ready)
🐙 New MPO trainer (multimodal by design, as in the paper)
📝 Full release notes here: https://github.com/huggingface/trl/releases/tag/v0.20.0
👁️ VLM support for GRPO (highly requested by the community!)
🎞️ New GSPO trainer (from @Qwen , released last week, VLM-ready)
🐙 New MPO trainer (multimodal by design, as in the paper)
📝 Full release notes here: https://github.com/huggingface/trl/releases/tag/v0.20.0
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💬 From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
upvoted
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AI Companionship: Why We Need to Evaluate How AI Systems Handle Emotional Bonds
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𝗙𝗶𝗿𝘀𝘁 𝗚𝗣𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝘄𝗶𝘁𝗵 𝗘𝗨 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲? 🇪🇺
With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! 📊📚)
The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd 👀
In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month 🤗 ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too 💡)
Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations 🙌 Definitely a step forward for transparency 🔍
To learn more have a look at:
- The SmolLM3 model: HuggingFaceTB/SmolLM3-3B
- Its filled out Public Summary of Training Content: hfmlsoc/smollm3-eu-data-transparency
- And if you're interested, some previous remarks on regulatory minimum meaningful standards for data disclosure: https://huggingface.co/blog/yjernite/naiac-data-transparency
With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! 📊📚)
The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd 👀
In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month 🤗 ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too 💡)
Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations 🙌 Definitely a step forward for transparency 🔍
To learn more have a look at:
- The SmolLM3 model: HuggingFaceTB/SmolLM3-3B
- Its filled out Public Summary of Training Content: hfmlsoc/smollm3-eu-data-transparency
- And if you're interested, some previous remarks on regulatory minimum meaningful standards for data disclosure: https://huggingface.co/blog/yjernite/naiac-data-transparency