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Adina Yakefu

AdinaY

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upvoted a paper about 1 hour ago

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

reacted to lin-tan's post with 🔥 about 22 hours ago

🚀 Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! 🔗 Preprint: https://arxiv.org/pdf/2409.19471 We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. 🤖 💡SELP presents three key insights: 1️⃣ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications. 2️⃣ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL. 3️⃣ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency. 📊 Experiment: Our experiments demonstrate SELP’s effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate. @yiwu @jiang719 #ICRA2025 #LLM #Robotics #Agent #LLMPlanner

reacted to their post with 🔥 about 22 hours ago

Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community. https://huggingface.co/collections/shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e ✨ 7B/72B ✨ Apache 2.0 ✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto

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upvoted a paper about 1 hour ago

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Paper • 2502.02737 • Published 1 day ago • 61

reacted to lin-tan's post with 🔥 about 22 hours ago

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1340

🚀 Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! 🔗 Preprint: https://arxiv.org/pdf/2409.19471

We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. 🤖

💡SELP presents three key insights:
1️⃣ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications.
2️⃣ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL.
3️⃣ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency.

📊 Experiment: Our experiments demonstrate SELP’s effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate.

@yiwu @jiang719

#ICRA2025 #LLM #Robotics #Agent #LLMPlanner

reacted to their post with 🔥 about 22 hours ago

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1139

Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
✨ 7B/72B
✨ Apache 2.0
✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto

posted an update about 22 hours ago

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1139

upvoted a collection about 22 hours ago

Xwen-Chat

Collection

6 items • Updated 3 days ago • 6

upvoted an article 1 day ago

Article

Open-source DeepResearch – Freeing our search agents

3 days ago

• 635

reacted to m-ric's post with 🚀🔥 1 day ago

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5530

Introducing 𝗼𝗽𝗲𝗻 𝗗𝗲𝗲𝗽-𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 by Hugging Face! 💥

OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.

⏱️ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! ⏱️

➡️ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...

We aimed for the best performance: are the agent's answers really rigorous?

On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
➡️ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution 💪💪

And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !

Read the blog post 👉 https://huggingface.co/blog/open-deep-research

reacted to albertvillanova's post with 🤗 1 day ago

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1727

🚀 Introducing @huggingface Open Deep-Research💥

In just 24 hours, we built an open-source agent that:
✅ Autonomously browse the web
✅ Search, scroll & extract info
✅ Download & manipulate files
✅ Run calculations on data

55% on GAIA validation set! Help us improve it!💡
https://huggingface.co/blog/open-deep-research

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reacted to victor's post with 🔥❤️ 1 day ago

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2775

Hey everyone, we've given https://hf.co/spaces page a fresh update!

Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.

New Categories: Check out the cool new filter bar with icons to help you pick a category fast.

Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.

Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.

We’d love to hear what you think—drop us some feedback plz!

5 replies

reacted to ZhengPeng7's post with 🔥👍 1 day ago

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1353

We just released the [BiRefNet_HR]( ZhengPeng7/BiRefNet_HR) for general use on higher resolution images, which was trained with images in 2048x2048. If your images are mostly larger than 1024x1024, use BiRefNet_HR for better results! Thanks to @Freepik for the kind support of H200s for this huge training.

HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.

Here is a comparison between the results of the original one and the new HR one on HR inputs:

And, the performance of this new HR one and the previous one trained in 1024x1024 on val set: