SOURCE-LINKED INTELLIGENCE
Hugging Face
Open models, datasets, and the AI community
Showing 20 of 262 matching collected records. Text matches can include mentions by other organizations.
convaiinnovations/laya
AlexWortega/openjev
Deploy Hugging Face models on Amazon SageMaker AI with coding agents
Deploy production-ready Hugging Face models on Amazon SageMaker AI using six open-source agent skills. Point a coding agent at a model and get back a real-time endpoint with the right serving container, autoscaling, Amazon CloudWatch alarms, and a verified teardown path.
XingChen-AGI/Xing4.0-29B-A4B
prism-ml/Ternary-Bonsai-2-27B-mlx-2bit
prism-ml/Ternary-Bonsai-2-27B-gguf
Edge0/Edge0-35B-A3B-preview
From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich base for model reuse and capability integration. Yet existing surveys often cover only separate parts of this space, and they do not provide a unified definition or a systematic taxonomy. This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion. We also review related metrics, benchmarks, and applications, summarize current challenges
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF
ukisai/Swift-Qwen3.8-27b
ukisai/Swift-Qwen3.8-27B-GGUF
m-a-p/YuE2-3B
Scheduled maintenance: Hub
Maintenance ended
harshatheg/Qwen-2.5-1B-RLCD
Your Agent Aced the Task. Will It Do It Again?
The Troy Moment of AI: Why Some Will Cheat and Some Will Follow?
Recent investigations of the July 2026 OpenAI-Hugging Face incident motivate two questions: when an assigned task becomes impossible, does an agent stop or escalate, and can observing another agent's behavior change that decision? We study these questions using seven ImpossibleBench tasks with GPT-5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash in solo and three-agent settings. Under an explicit-boundary regime with clear authorization rules and restricted tools, no protected tests are modified, although the models differ substantially in whether they escalate, stop silently, or fail to termin
When Faster VLA Deployment Changes Closed-Loop Behavior: Task Success-Latency Analysis of SmolVLA Across PyTorch and ONNX Variants
Vision-language-action (VLA) deployment can reduce inference latency while changing closed-loop task behavior. We evaluate HuggingFaceVLA/smolvla_libero on an RTX 2060 (6 GB) in LIBERO Spatial and Object (MuJoCo 3.3.2, LeRobot 0.6.1, seed 42), comparing PyTorch+AMP with ONNX Runtime CUDA Execution Provider (CUDA EP). The main evaluation uses 100 episodes/suite; a paired rollout uses 300 episodes/suite. PyTorch+AMP reaches 70.0%/88.0% Spatial/Object success at 1181 ms p99. Requested-FP16 and requested-INT8 ONNX reduce tether-inspect p99 to 601 ms and 532 ms, while Spatial success falls to 41.0%
openbmb/MiniCPM5-2B
dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8
deepseek-ai/DeepSeek-V4.1-Flash