GLM-5.3-Flash-EXL3
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Qwen3.6-27B-Uncensored-Cyber-W4A16-AWQ
philbert440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-Uncensored-Aggressive
philbert440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-Uncensored-Aggressive-GGUF
philbert440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-Uncensored-Aggressive-W4A16-AWQ
philbert4404-bit weight (W4A16) AWQ quant of **Qwen3.8-27B-Uncensored-Aggressive** (α=1.15 recipe update), in `compressed-tensors` format. Vision tower and the grafted **MTP head** are preserved. Recipe update to the Aggressive line: the previous build ablated at α≈1.24, which a larger benchmark sweep showed **over-ablates** past the ~1.15 quality peak. This build uses **α=1.15** — more open and better on every measured axis. Quant: AWQ, group size 128, asymmetric, MSE observer, 256 Magpie-CoT calibration samples @1024 tokens.
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View details ↗Qwen3.8-27B-Uncensored-Cyber
philbert440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-Uncensored-Cyber-GGUF
philbert440llama.cpp GGUF quants of **Qwen3.8-27B-Uncensored-Cyber** (v2 recipe), with the **vision projector** (`mmproj-*`) for image input and the **MTP head** (`mtp-*`) for speculative decoding. Full BF16 is not shipped as a single GGUF (exceeds the 50GB per-file limit) — use `Q8_0` (≈lossless) or the bf16 safetensors repo. Uncensored / de-refused, tuned to fully answer cyber and offensive-security questions. Use responsibly and in compliance with applicable law.
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View details ↗Qwen3.8-27B-Uncensored-Cyber-MLX-4bit
philbert440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-Uncensored-Cyber-NVFP4
philbert440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-Uncensored-Cyber-W4A16-AWQ
philbert440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-W4A16-AWQ
philbert440INT4 weight-only quantization of Qwen/Qwen3.8-27B — **the whole model, nothing stripped**: vision tower intact, MTP speculative-decoding head intact, thinking mode calibrated with real reasoning traces. 55 GB BF16 → **19.5 GB**, which puts a current-generation 27B VLM (with headroom for 32K+ context) on a pair of 32 GB V100s — and it serves on modern GPUs with stock vLLM, no ceremony. Validated end-to-end on **2×V100-32GB under 1Cat-vLLM 1.2.2** (SM70 TurboMind dequant path): coherence, think-tag discipline, image understanding, math, long-form generation, and MTP speculative decoding in both draft modes. **Methodology:** warm serve (2 discarded warmup generations), fixed-length generations via `ignore_eos` so every run produces exactly the stated token count, official Qwen3.8 sampling per mode (thinking `1.0/0.95/20`, instruct `0.7/0.80/20 + presence 1.5`), varied prompts. Reported as mean ± sd tokens/s. Rig: 2×V100-32GB, 1Cat-vLLM 1.2.2, TP2, `fp8_e5m2` KV, `max_num_seqs 4`, MTP K=2.
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