Agents of Chaos
Paper
• 2602.20021 • Published
• 29
AI & ML interests
A one-year long research workshop on large language models: the Summer of Language Models 21 🌸
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pretokenizer Regex issues?
8
#278 opened over 1 year ago
by
hpcpony
gsarti
authored 2 papers 4 days ago
Test PR
#286 opened 6 days ago
by
FIRSTACCOUNT69
Test discussion
#287 opened 6 days ago
by
FIRSTACCOUNT69
Test discussion
#288 opened 6 days ago
by
FIRSTACCOUNT69
albertvillanova
posted an
update 8 days ago
Post
1748
🚀 TRL v0.29.0 introduces trl-training: an agent-native training skill.
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
The future of ML tooling is agent-native.
🔗 https://github.com/huggingface/trl/releases/tag/v0.29.0
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
The future of ML tooling is agent-native.
🔗 https://github.com/huggingface/trl/releases/tag/v0.29.0
armanc
authored a paper 14 days ago
albertvillanova
posted an
update 23 days ago
Post
1726
5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
Grateful to be part of such an awesome team making it happen every day.
archiki
submitted a paper to Daily Papers 23 days ago
pjox
authored a paper about 1 month ago
armanc
authored a paper about 2 months ago
Fails to load with transformers v4.57+
4
#14 opened 3 months ago
by
qgallouedec
monsoon-nlp
posted an
update 3 months ago
Post
440
PatchDNA, a DNA foundation model based on Meta's BLT tokenization strategy https://www.biorxiv.org/content/10.1101/2025.11.28.691095v1
BramVanroy
posted an
update 5 months ago
Post
541
What are currently the best multilingual models with at most 72B parameters? Are Llama 3.3 70B and Qwen 2.5 72B still king?
- 1 reply
Post
4586
🌎 AI ethics and sustainability are two sides of the same coin.
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
- 1 reply
Post
11060
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
- 7 replies
monsoon-nlp
posted an
update 5 months ago
Post
465
Bio LLMs train on many genomes, but can we encode differences within a species? TomatoTomato adds pangenome tokens to represent a domestic tomato and a wild tomato in one sequence 🍅 🧬
monsoon-nlp/tomatotomato-gLM2-150M-v0.1
monsoon-nlp/tomatotomato-gLM2-150M-v0.1
Post
428
I've noticed something. While we're careful about what we post on social media, we're sharing our deepest and most intimate thoughts with AI chatbots -- health concerns, financial worries, relationship issues, business ideas...
With OpenAI hinting at ChatGPT advertising, this matters more than ever. Unlike banner ads, AI advertising happens within the conversation itself. Sponsors could subtly influence that relationship advice or financial guidance.
The good news? We have options.
🤝 Open source AI models let us keep conversations private, avoid surveillance-based business models, and build systems that actually serve users first.
Read more about it in our latest blog post, co-written with
@frimelle
https://huggingface.co/blog/giadap/privacy-conversational-ai
With OpenAI hinting at ChatGPT advertising, this matters more than ever. Unlike banner ads, AI advertising happens within the conversation itself. Sponsors could subtly influence that relationship advice or financial guidance.
The good news? We have options.
🤝 Open source AI models let us keep conversations private, avoid surveillance-based business models, and build systems that actually serve users first.
Read more about it in our latest blog post, co-written with
@frimelle
https://huggingface.co/blog/giadap/privacy-conversational-ai