Qwen3.8-27B-Uncensored-YMQ
from JonathanColetti/Qwen3.8-27B-Uncensored
qwen3.8-27b-uncensored-modelopt is a abliterated and uncensored variant of JonathanColetti/Qwen3.8-27B-Uncensored, published on Hugging Face by joshebbs. It is 27B parameters, 48GB VRAM class class and apache-2.0 licence. It is distributed across 2 repositories in NVFP4 format, totalling 21.7K downloads.
How qwen3.8-27b-uncensored-modelopt was modified, and what that implies.
Abliteration + uncensored fine-tune
Directional ablation combined with additional fine-tuning on unfiltered data, so behaviour diverges from the base model beyond refusal removal alone.
Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.
From the publisher’s model card
“produced with NVIDIA TensorRT Model Optimizer 0.43.0 for Blackwell-class inference under vLLM.”
Commands are templates — confirm the exact repository and quant file before use.
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "joshebbs/qwen3.8-27b-uncensored-nvfp4-modelopt"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype="auto", device_map="auto"
)vllm serve joshebbs/qwen3.8-27b-uncensored-nvfp4-modelopt --trust-remote-codeThe only published repository of qwen3.8-27b-uncensored-modelopt.
| Repository | Format | Downloads |
|---|---|---|
| joshebbs/qwen3.8-27b-uncensored-nvfp4-modeloptsource | NVFP4 | 21.7K |
Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.
Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.
Metadata for qwen3.8-27b-uncensored-modelopt is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with joshebbs, and does not independently verify publisher claims. See responsible use.