RoboBrain
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Adina Yakefu
AdinaY
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upvoted
a
collection
about 4 hours ago
RoboBrain
upvoted
a
paper
about 5 hours ago
AniSora: Exploring the Frontiers of Animation Video Generation in the
Sora Era
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an
update
about 5 hours ago
RoboBrain 🧠 an 32B open embodied AI model enabling multi-robot collaboration, released by BAAIBeijing.
Model: https://huggingface.co/collections/BAAI/robobrain-681e1389c64d06b3e4a45e44
Dataset: https://huggingface.co/datasets/BAAI/ShareRobot
✨ Task decomposition into 20+ precise actions
✨ Operable region detection (e.g: teapot handles, drawers)
✨ Motion trajectory prediction to avoid collisions
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AdinaY's activity
upvoted
a
collection
about 4 hours ago
upvoted
a
paper
about 5 hours ago
posted
an
update
about 5 hours ago
Post
385
RoboBrain 🧠 an 32B open embodied AI model enabling multi-robot collaboration, released by BAAIBeijing.
Model: BAAI/robobrain-681e1389c64d06b3e4a45e44
Dataset: BAAI/ShareRobot
✨ Task decomposition into 20+ precise actions
✨ Operable region detection (e.g: teapot handles, drawers)
✨ Motion trajectory prediction to avoid collisions
Model: BAAI/robobrain-681e1389c64d06b3e4a45e44
Dataset: BAAI/ShareRobot
✨ Task decomposition into 20+ precise actions
✨ Operable region detection (e.g: teapot handles, drawers)
✨ Motion trajectory prediction to avoid collisions
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about 5 hours ago
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367
Seed-Coder 💻 code models by ByteDance
ByteDance-Seed/seed-coder-680de32c15ead6555c75b0e4
✨ 8B models: base/instruct/reasoning
✨ MIT licensed
✨ Model-centric data filtering (less manual effort)
ByteDance-Seed/seed-coder-680de32c15ead6555c75b0e4
✨ 8B models: base/instruct/reasoning
✨ MIT licensed
✨ Model-centric data filtering (less manual effort)
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about 6 hours ago
upvoted
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liked
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about 6 hours ago
posted
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3 days ago
Post
1992
HunyuanCustom 🔥 a multimodal video generation framework supporting image, audio, video & text conditions, released by TencentHunyuan
tencent/HunyuanCustom
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation (2505.04512)
✨Strong Identity Consistency
✨SOTA outperforms
tencent/HunyuanCustom
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation (2505.04512)
✨Strong Identity Consistency
✨SOTA outperforms
updated
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liked
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3 days ago
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merve's
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5 days ago
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4902
A ton of impactful models and datasets in open AI past week, let's summarize the best 🤩
merve/releases-apr-21-and-may-2-6819dcc84da4190620f448a3
💬 Qwen made it rain! They released Qwen3: new dense and MoE models ranging from 0.6B to 235B 🤯 as well as Qwen2.5-Omni, any-to-any model in 3B and 7B!
> Microsoft AI released Phi4 reasoning models (that also come in mini and plus sizes)
> NVIDIA released new CoT reasoning datasets
🖼️ > ByteDance released UI-TARS-1.5, native multimodal UI parsing agentic model
> Meta released EdgeTAM, an on-device object tracking model (SAM2 variant)
🗣️ NVIDIA released parakeet-tdt-0.6b-v2, a smol 600M automatic speech recognition model
> Nari released Dia, a 1.6B text-to-speech model
> Moonshot AI released Kimi Audio, a new audio understanding, generation, conversation model
👩🏻💻 JetBrains released Melium models in base and SFT for coding
> Tesslate released UIGEN-T2-7B, a new text-to-frontend-code model 🤩
💬 Qwen made it rain! They released Qwen3: new dense and MoE models ranging from 0.6B to 235B 🤯 as well as Qwen2.5-Omni, any-to-any model in 3B and 7B!
> Microsoft AI released Phi4 reasoning models (that also come in mini and plus sizes)
> NVIDIA released new CoT reasoning datasets
🖼️ > ByteDance released UI-TARS-1.5, native multimodal UI parsing agentic model
> Meta released EdgeTAM, an on-device object tracking model (SAM2 variant)
🗣️ NVIDIA released parakeet-tdt-0.6b-v2, a smol 600M automatic speech recognition model
> Nari released Dia, a 1.6B text-to-speech model
> Moonshot AI released Kimi Audio, a new audio understanding, generation, conversation model
👩🏻💻 JetBrains released Melium models in base and SFT for coding
> Tesslate released UIGEN-T2-7B, a new text-to-frontend-code model 🤩
reacted to
merve's
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6 days ago
Post
6406
A real-time object detector much faster and accurate than YOLO with Apache 2.0 license just landed to Hugging Face transformers 🔥
D-FINE is the sota real-time object detector that runs on T4 (free Colab) 🤩
> Collection with all checkpoints and demo ustc-community/d-fine-68109b427cbe6ee36b4e7352
Notebooks:
> Tracking https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_tracking.ipynb
> Inference https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_inference.ipynb
> Fine-tuning https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_finetune_on_a_custom_dataset.ipynb
h/t @vladislavbro @qubvel-hf @ariG23498 and the authors of the paper 🎩
Regular object detectors attempt to predict bounding boxes in (x, y, w, h) pixel perfect coordinates, which is very rigid and hard to solve 🥲☹️
D-FINE formulates object detection as a distribution for bounding box coordinates, refines them iteratively, and it's more accurate 🤩
Another core idea behind this model is Global Optimal Localization Self-Distillation ⤵️
this model uses final layer's distribution output (sort of like a teacher) to distill to earlier layers to make early layers more performant.
D-FINE is the sota real-time object detector that runs on T4 (free Colab) 🤩
> Collection with all checkpoints and demo ustc-community/d-fine-68109b427cbe6ee36b4e7352
Notebooks:
> Tracking https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_tracking.ipynb
> Inference https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_inference.ipynb
> Fine-tuning https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_finetune_on_a_custom_dataset.ipynb
h/t @vladislavbro @qubvel-hf @ariG23498 and the authors of the paper 🎩
Regular object detectors attempt to predict bounding boxes in (x, y, w, h) pixel perfect coordinates, which is very rigid and hard to solve 🥲☹️
D-FINE formulates object detection as a distribution for bounding box coordinates, refines them iteratively, and it's more accurate 🤩
Another core idea behind this model is Global Optimal Localization Self-Distillation ⤵️
this model uses final layer's distribution output (sort of like a teacher) to distill to earlier layers to make early layers more performant.
- 2 replies
posted
an
update
6 days ago
Post
3840
ACE-Step 🎵 a music generation foundation model released by
StepFun & ACEStudio
Model: ACE-Step/ACE-Step-v1-3.5B
Demo: ACE-Step/ACE-Step
✨ 3.5B, Apache2.0 licensed
✨ 115× faster than LLMs (4-min music in 20s on A100)
✨ Diffusion + DCAE + linear transformer = speed + coherence
✨ Supports voice cloning, remixing, lyric editing & more
StepFun & ACEStudio
Model: ACE-Step/ACE-Step-v1-3.5B
Demo: ACE-Step/ACE-Step
✨ 3.5B, Apache2.0 licensed
✨ 115× faster than LLMs (4-min music in 20s on A100)
✨ Diffusion + DCAE + linear transformer = speed + coherence
✨ Supports voice cloning, remixing, lyric editing & more
- 1 reply
posted
an
update
6 days ago
Post
793
CCI4.0-M2 📊 A powerful dataset with 3 specialized subsets, released by
BAAIBeijing
BAAI/cci40-68199d90bbc798680df16d7c
✨ M2-Base: 3.5TB web data (EN/ZH), with LLM-augmented content, APACHE2.0
✨ M2-CoT: 4.2TB of auto-synthesized CoT reasoning data
✨ M2-Extra: domain-specific knowledge
BAAIBeijing
BAAI/cci40-68199d90bbc798680df16d7c
✨ M2-Base: 3.5TB web data (EN/ZH), with LLM-augmented content, APACHE2.0
✨ M2-CoT: 4.2TB of auto-synthesized CoT reasoning data
✨ M2-Extra: domain-specific knowledge