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zeeksa/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-FP8-Dynamic
Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-FP8-Dynamic is a machine learning model from zeeksa. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
vLLM-native FP8 quantization (compressed-tensors, data-free dynamic scheme) of the HauhauCS uncensored finetune.
Downloads · 30 days
21
1% of all-time downloads
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27.8B
61.6 GB on disk
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.safetensors31.2 GB · 100%
How the weights are stored.
F8_E4M324.4B · 88%
From the Hugging Face model README
vLLM-native FP8 quantization (compressed-tensors, data-free dynamic scheme) of the HauhauCS uncensored finetune.
Provenance & fidelity: HauhauCS released this finetune only as GGUF — no safetensors
source exists publicly. These weights were dequantized from the highest-fidelity public
artifact (Q8_K_P GGUF, which internally is Q8_0/F16/F32 tensors) back into the official
Qwen/Qwen3.6-27B HF layout, with every tensor validated against the stock checkpoint's
exact shape and dtype (1199/1199). Quality ceiling is therefore Q8 (near-lossless vs the
author's private BF16), not true BF16.
mtp.*, taken from stock Qwen/Qwen3.6-27B since the finetune
never shipped one) — enables speculative decoding in engines that support Qwen3.6 MTP.Qwen/Qwen3.6-27B (the finetune does not modify them).vllm serve zeeksa/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-FP8-Dynamic
compressed-tensors FP8_DYNAMIC (float-quantized weights, dynamic per-token activations).
Linear layers FP8; lm_head and the vision tower kept BF16. ~31 GB.
Note: Qwen3.6's gated attention is new — use a recent transformers (>=5.10) / recent vLLM; older engine versions may not support the architecture.