cesarsal1nas/Orca-Cyber-27B-XE2MX4-GGUF
text-generation model by cesarsal1nas
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Unrotated XE2MX4 requant of the Orca Cyber IQ4XS GGUF, built for Intel Xe2 dGPUs through the SYCL backend in llama.cpp-exl3xe2cuda. This is a custom GGUF type, stock llama.cpp does not load it, CUDA and Vulkan do not run it. It is not EXL3 or MXFP4, each block stores 32 signed 4-bit weights and one FP16 scale, 4.5 bits per weight. Output and token embeddings stay Q80, embedded MTP is retained. The XE2MX4 loader and quantizer are now on the GitHub fork's main branch, use that fork rather than stock llama.cpp. Held-out wikitext-2 test, 100 chunks of 512 tokens, lower PPL is better: S48R is what we used locally before this file, we switched to unrotated because PPL was better at similar speed. This is a second quantization from IQ4XS, not a BF16 comparison, the PPL values are prior measurements on the same test text and do not guarantee identical results…
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Model overview
cesarsal1nas/Orca-Cyber-27B-XE2MX4-GGUF is a text-generation model repository published by cesarsal1nas on Hugging Face. The source reports the gguf library.
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Model facts
- Task
- text-generation
- Library
- gguf
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-10-13T06:30:04.000Z
- Last modified
- 2026-10-13T06:37:25.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-13T06:41:58.956Z. Daily imports are snapshots, not real-time monitoring.
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