inclusionAI/gemma-4-26B-A4B-it-singprobe开源了新模型

InclusionAI inclusionAI/gemma-4-26B-A4B-it-singprobe inclusionAI/gemma-4-26B-A4B-it-singprobe https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe

inclusionAI/gemma-4-26B-A4B-it-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) / gemma-4-26B-A4B-it-singprobe (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-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) gemma-4 (https://huggingface.co/models?other=gemma-4) custom_code (https://huggingface.co/models?other=custom_code) arxiv: 2608.30703 License: apache-2.0 Model card (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe) Files Files and versions xet (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe/tree/main) Community (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe/discussions) Copy to bucket new gemma-4-26B-A4B-it-singprobe (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#gemma-4-26b-a4b-it-singprobe) Model Description (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#model-description) Evaluation (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#evaluation) Quick Start (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#quick-start) Citation (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#citation) (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#gemma-4-26b-a4b-it-singprobe) gemma-4-26B-A4B-it-singprobe (https://huggingface.co/inclusionAI/gemma-4-26B-A4B-it-singprobe#model-description) Model Description SingProbe is an intrinsic streaming guardrail built on google/gemma-4-26B-A4B-it. 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/gemma-4-26B-A4B-it-singprobe 5.67M [8, 18, 28] 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/gemma-4-26B-A4B-it-singprobe#evaluation) Evaluation Higher is better for every metric. Results are average