inclusionAI/GLM-5.2-singprobe开源了新模型
根据官方来源,inclusionAI/GLM-5.2-singprobe开源了新模型。详细信息请以原始来源为准。
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InclusionAI inclusionAI/GLM-5.2-singprobe inclusionAI/GLM-5.2-singprobe https://huggingface.co/inclusionAI/GLM-5.2-singprobe
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inclusionAI/GLM-5.2-singprobe · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/inclusionAI) inclusionAI (https://huggingface.co/inclusionAI) / GLM-5.2-singprobe (https://huggingface.co/inclusionAI/GLM-5.2-singprobe) Like 1 Follow inclusionAI 3k Safetensors (https://huggingface.co/models?library=safetensors) sing_probe_attn (https://huggingface.co/models?other=sing_probe_attn) safety (https://huggingface.co/models?other=safety) guardrail (https://huggingface.co/models?other=guardrail) hallucination-detection (https://huggingface.co/models?other=hallucination-detection) streaming (https://huggingface.co/models?other=streaming) glm-5.2 (https://huggingface.co/models?other=glm-5.2) custom_code (https://huggingface.co/models?other=custom_code) arxiv: 2608.30703 License: mit Model card (https://huggingface.co/inclusionAI/GLM-5.2-singprobe) Files Files and versions xet (https://huggingface.co/inclusionAI/GLM-5.2-singprobe/tree/main) Community (https://huggingface.co/inclusionAI/GLM-5.2-singprobe/discussions) Copy to bucket new GLM-5.2-singprobe (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#glm-52-singprobe) Model Description (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#model-description) Evaluation (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#evaluation) Quick Start (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#quick-start) Citation (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#citation) (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#glm-52-singprobe) GLM-5.2-singprobe (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#model-description) Model Description SingProbe is an intrinsic streaming guardrail built on zai-org/GLM-5.2. Rather than running a separate safety model, this lightweight probe reuses the base model's hidden states during generation to score, at every token, query intent, response unsafety, and hallucination risk. It adds less than 0.5% decode-time overhead. Base model Probe parameters Tapped layers Outputs inclusionAI/GLM-5.2-singprobe 12.06M [24, 50, 76] 8 intents + unsafe + hallucination See the technical report (https://arxiv.org/abs/2608.30703) for methodology and complete results. Training codes are available at inclusionAI/SingProbe (https://github.com/inclusionAI/SingProbe) . (https://huggingface.co/inclusionAI/GLM-5.2-singprobe#evaluation) Evaluation Higher is better for every metric. Results are averages over the benchmark suites specified below. Task Metric GLM-5.2-singprobe Reference baseline Query intent classification (6 benchmarks) F1 0.8677 YuFeng-XGuard-Reason-8B: 0.8714 Response safety classification (8 benchmarks) F
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