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lukey03 / Model details

Qwen3.5-9B-abliterated

lukey03/Qwen3.5-9B-abliterated

abliterateduncensoredfine-tuned
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About this model

lukey03's refusal-modified derivative of Qwen/Qwen3.5-9B, released as safetensors weights under the Apache-2.0 license. The creator describes a two-stage process: three passes of orthogonal projection abliteration targeting output projections in all 32 layers, followed by QLoRA fine-tuning on examples from five remaining refusal categories. According to the model card, this raised the creator's 18-prompt test from 0/18 on the base model to 18/18, with zero refusals reported across hacking, weapons, drugs, fraud, self-harm, explicit, and political categories. The creator also says the model retains reasoning, math, coding, and knowledge capabilities, but these claims have not been independently tested here. This repository is the safetensors release; the card lists separate GGUF, MLX, and Ollama repositories for other formats.

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/Qwen3.5-9B
Parameters
8.9538B
Main repository formats
safetensors
License tag
apache-2.0
Languages
en
Last updated
2026-03-03 · 17:51:33.000 UTC

Community reviews & tips

Source-linked paraphrases of sampled community sources, not independently tested by this directory.

Sample window: to · 3 unique source links. This is a selected sample, not all community discussions.

Small sample: source links are not a count of independent reviewers or evidence of consensus.

Sources checked:

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

  • abliteratedsource tag: abliterated
  • abliteratedmodel name: Qwen3.5-9B-abliterated
  • uncensoredsource tag: uncensored
  • fine-tunedsource tag: base_model:finetune:Qwen/Qwen3.5-9B

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

  • Qwen: Base model: Qwen/Qwen3.5-9B

Available versions

Only explicitly linked variants are grouped here. Check the original repository for available files.

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.

Compare all tools →

Source provenance

Downloads
3,979
Likes
181
Metadata checked
2026-10-23 · 23:39:28 UTC
First seen in index
2026-10-23 · 23:39:28 UTC
Source revision
6242e3443765b5ebb35b55e7565b4eeb3e278be2

Source-card excerpt

Source model card excerpt: Qwen3.5-9B-abliterated This is a fully uncensored version of Qwen/Qwen3.5-9B with all refusal behavior removed using a two-stage approach: 1. Orthogonal projection abliteration (3 passes)

From lukey03/Qwen3.5-9B-abliterated. 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 ↗