Qwen3-Omni-30B-A3B-Instruct-GGUF
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CALM3-22B-Chat-GGUF
grapevine-AI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lumeleto
gratefulasi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-mini-toxicity
gravitee-io基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-small-pii-detection
gravitee-ioToken-classification model for PII detection, fine-tuned from `prajjwal1/bert-small` on `gravitee-io/pii-detection-dataset`. Detect personally identifiable information (PII) spans in `english` text. Suitable for privacy filtering, redaction pipelines, and data-leak prevention particularly on structured data (JSON, HTML, XML, SQL, Document) * English-focused; other languages will degrade * Domain drift is real: audit on your own data ---
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View details ↗bert-small-toxicity
gravitee-io基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗detoxify-onnx
gravitee-io基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗distilbert-multilingual-toxicity-classifier
gravitee-io基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Llama-Prompt-Guard-2-22M-onnx
gravitee-ioThis repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-22M You can find the full source code, CLI tools, and evaluation scripts in the official GitHub repository.
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View details ↗Llama-Prompt-Guard-2-86M-onnx
gravitee-ioThis repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-86M You can find the full source code, CLI tools, and evaluation scripts in the official GitHub repository.
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View details ↗albedobase2-xl
GraydientPlatformAPI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗boltning-hyperd-sdxl
GraydientPlatformAPI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗picx-real
GraydientPlatformAPI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗realisian6
GraydientPlatformAPI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Llama-3-8B-Instruct-RR
GraySwanAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ProLLaMA
GreatCaptainNemo基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ProLLaMA_Stage_1
GreatCaptainNemo基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-Intel-Arc-Tuned-GGUF
Greatjedi**Hardware-Accelerated Weights for Intel Arc GPUs (Battlemage Xe2 / Alchemist Xe / oneAPI SYCL)** *Engineered by Greatjedi & Gemini (DeepMind Antigravity Team)* If you own an **Intel Arc GPU** (such as the **Intel Arc Pro B60 24GB**, Arc A770 16GB, or Battlemage graphics cards) and have attempted to run popular 27B quantization variants (such as `IQ2_S`, `IQ3_XXS`, or Unsloth's "Dynamic" `UD-Q4_K_S`), you likely noticed your generation speed crawling at an agonizing **8.1 to 12.2 tokens/second**, despite the GPU having more than enough raw compute and 512 GB/s memory bandwidth. 1. **Intel Arc Matrix Engines (XMX)** utilize **DPAS** (Dot Product Accumulate Systolic) hardware. DPAS requires dense, linearly indexed integers (`INT2`, `INT4`, `INT8`) to feed its systolic pipeline at thousands of operations per clock cycle. 2. **I-Quants (`IQ2_S`, `IQ3_XXS`, `IQ4_XS`, `IQ3_S`)** use non-linear 8-dimensional vector lattice codebooks ($E_8$). They require runtime table lookups per weight element. 3. In `llama.cpp`'s SYCL backend, **there are zero kernel implementations for I-Quants**. Whenever an IQ tensor is evaluated, SYCL drops out of hardware systolic GEMM into slow CPU/host dequantization loops. 4. **The Hidden Catch in Unsloth "UD" Models**: When we reverse-engineered Unsloth's popular `Qwen3.8-27B-UD-Q4_K_S.gguf`, we discovered it secretly contains **201 I-Quant tensors** (`IQ4_XS`, `IQ3_S`, `IQ3_XXS`), causing it to suffer from the exact same dequantization stall on Intel Arc!
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