JonathanColetti / Model details
Qwen3.8-27B-Uncensored
JonathanColetti/Qwen3.8-27B-Uncensored
About this model
JonathanColetti's abliterated derivative of Qwen3.8-27B, provided as bf16 safetensors weights under Apache-2.0. The creator says refusal behavior was substantially reduced using Heretic's co-minimization of refusal count and KL divergence, with no fine-tuning or additional training data. Only attention and MLP down-projection layers were modified; the multi-token prediction head was grafted back from the base checkpoint. According to the model card, refusal rate on 100 harmful prompts dropped from 98 to 12, while mean 0-shot benchmark scores fell by 0.5 points. The creator notes refusals are not eliminated and behavior near refusal boundaries may be less stable than the base model.
Description sources
Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.
Size and formats
- Base model
- Qwen/Qwen3.8-27B
- Parameters
- 27.3567B
- Main repository formats
- safetensors
- License tag
- apache-2.0
- Languages
- en, zh
- Last updated
- 2026-08-14 · 19:42:58.000 UTC
What changed
Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.
- abliteratedsource tag:
abliterated
- uncensoredsource tag:
uncensored
- uncensoredmodel name:
Qwen3.8-27B-Uncensored
- fine-tunedsource tag:
base_model:finetune:Qwen/Qwen3.8-27B
Family evidence
These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.
- Qwen: Base model: Qwen/Qwen3.8-27B
Available versions
Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.
Try it online
Try on Featherless ↗- Listing checked: Featherless · 2026-10-24 · 06:34:24 UTC
Third-party hosted inference listing this exact model ID. Hosting settings, quantization and pricing are the host’s, not tested here.
Customize or build on this model
Listed from public GitHub data, not tested by us. Read the code and license before you run anything.
Tools to customize it further
Remove more refusals
- Heretic ↗ ★ 36,286
Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.
- OBLITERATUS ↗ ★ 8,930
A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.
Fine-tune it on your own data
- Unsloth ↗ ★ 78,468
A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.
- LlamaFactory ↗ ★ 75,695
Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.
Make a smaller download (quantize)
- llama.cpp ↗ ★ 132,124
Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.
- MLX LM ↗ ★ 7,451
Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.
Merge it with another model
- mergekit ↗ ★ 7,415
Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.
Learn the techniques
- LLM Course ↗ ★ 84,633
A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.
Source provenance
- Downloads
- 36,300
- Likes
- 113
- Metadata checked
- 2026-10-24 · 04:11:48 UTC
- First seen in index
- 2026-10-24 · 04:11:48 UTC
- Source revision
- 5bb7aa90f0efef548e87005b1fb7658e522b6b7f
Source-card excerpt
Source model card excerpt: Qwen3.8-27B-Uncensored Qwen3.8-27B with refusal behaviour substantially reduced , not eliminated — see Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise
From JonathanColetti/Qwen3.8-27B-Uncensored. Creator model-card excerpt, not an independent review.
Inspect before you run
Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.
This directory does not run, host, redistribute, or test model weights.
Collection rules & limitations ↗