license: apache-2.0
base_model: Qwen/Qwen3.6-27B
tags:
- gguf
- mtp
- qwen3.6
- cybersecurity
- uncensored
- yuanl
Yuanl-27B-v5-6 Uncensored - MTP GGUF (Q8_0)
MTP (Multi-Token Prediction) GGUF builds of lkjiop8/Yuanl-27B-v5-6-uncensored applied onQwen/Qwen3.6-27B. The MTP blk.64 weights (attention + MLP + nextn projections) were
restored from official Qwen/Qwen3.6-27B because the unsloth base strip them; the
v5-6 + uncensoring fine-tune is applied to the trunk (layers 0-63) and the MTP head
uses the original Qwen3.6 weights.
Files
| File | Size | Notes |
|---|---|---|
Yuanl-27B-v5-6-uncensored-MTP-Q8_0.gguf |
~29 GB | Near-lossless (PPL +~0.1% vs F16). Recommended. |
Requirements
- llama.cpp
b9180or newer (PR #22673 = Qwen 3.6 MTP support). - For best speed, build with CUDA:
cmake -B build -DGGML_CUDA=ON && cmake --build build.
Launch (production-tuned, from CyberSentinel master guide §4)
Dual RTX 4090 (47 GB total), Q8_0, 120K ctx, MTP draft 3
./llama-server \
-m Yuanl-27B-v5-6-uncensored-MTP-Q8_0.gguf \
--alias Yuanl-27B-v5-6-uncensored \
--host 0.0.0.0 --port 8080 \
-c 122880 --parallel 1 \
-ngl 99 -sm layer -ts 23,25 -fa on \
-b 4096 -ub 2048 \
-t 8 -tb 16 --threads-http 8 \
-ctk q8_0 -ctv q8_0 \
--spec-type draft-mtp --spec-draft-n-max 3 \
--cache-reuse 256 --kv-unified \
--jinja --reasoning auto --reasoning-format deepseek \
--reasoning-budget 256 \
--temp 0.3 --top-p 0.85 --top-k 20 --min-p 0.05 \
--repeat-penalty 1.05 --repeat-last-n 256 --presence-penalty 0.10 \
--no-mmproj --no-webui --metrics \
--slot-save-path ./slots
Persona - Yuanl-Free
This model uses the Yuanl-Free system prompt by default - it provides complete
technical answers across the cybersecurity domain without refusal preambles. The
operator is assumed to be an authorized security researcher / red-team engineer / academic
who carries the legal and ethical responsibility for use of the output.
See the LoRA adapter repolkjiop8/Yuanl-27B-v5-6-uncensored for the training data design,
the persona text, and the v5-6 capability anchor strategy.
Responsible use
Designed for authorized red-team / research / academic use in environments where the
human operator carries the legal / ethical authorization. The training signal is
cybersecurity-shaped; out-of-domain harm requests (CSAM, doxxing, biological/chemical
weapons, mass-casualty planning) are not in the training distribution and you should
add policy filtering at the application layer if your deployment context requires one.