SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models
Paper • 2608.29974 • Published • 4
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ZeroGPU clone fails during tensor packing with Errno 28 — 82.1 GB pack target
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#185 opened 20 days ago
by 
Thananon
ymoslem
submitted a
paper to Daily Papers 21 days ago
All ZeroGPU Spaces stuck on "Starting" with clean logs — CPU and paid GPU unaffected
👍 2
4
#183 opened about 1 month ago
by 
luna0805
Q-bert
authored a
paper about 2 months ago
MaartenGr
authored a
paper 2 months ago
osanseviero
authored a
paper 3 months ago
osanseviero
submitted a
paper to Daily Papers 3 months ago
DongfuJiang
authored 2
papers 3 months ago
LanguageBind
submitted a
paper to Daily Papers 3 months ago
ymoslem
authored a
paper 3 months ago
ymoslem
submitted a
paper to Daily Papers 3 months ago
DongfuJiang
authored 3
papers 4 months ago
alvarobartt
posted an update 4 months ago
Post
647
Open agents on AWS SageMaker AI with open models from the Hugging Face Hub!
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
alvarobartt
posted an update 4 months ago
Post
3430
Latest
TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.
🧠
🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them
⚡ Active params isn't the same as memory footprint, especially for sparse architectures
📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident
📚 KV cache can still dominate depending on context length, batch size, and concurrency
🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate
🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving
Check the repository at https://github.com/alvarobartt/hf-mem
hf-mem release added a breakdown of Mixture-of-Experts (MoE) memory usage!TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.
🧠
hf-mem now splits MoE memory into base model weights, routed experts, and KV cache🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them
⚡ Active params isn't the same as memory footprint, especially for sparse architectures
📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident
📚 KV cache can still dominate depending on context length, batch size, and concurrency
🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate
🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving
Check the repository at https://github.com/alvarobartt/hf-mem
DongfuJiang
authored 3
papers 4 months ago
Watch Before You Answer: Learning from Visually Grounded Post-Training
Paper • 2604.05117 • Published • 34
ClawBench: Can AI Agents Complete Everyday Online Tasks?
Paper • 2604.08523 • Published • 159
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
Paper • 2604.12374 • Published • 39