mlabonne / Model details

Qwen3-1.7B-abliterated

mlabonne/Qwen3-1.7B-abliterated

abliteratedfine-tuned

Read the original model card ↗ · Choosing a model

About this model

Mlabonne's experimental refusal-modified Qwen3-1.7B, released as safetensors weights. The creator recommends temperature 0.6, top_k 20, top_p 0.95 and min_p 0, while noting that reasoning-mode behavior complicated refusal removal. The card's greater-than-90% acceptance rate is a research target, not a reported result for this checkpoint. Metadata reports BF16 tensors but the config specifies float32; runtime precision, memory needs, usable context and retained reasoning have not been tested here.

Description sources

https://huggingface.co/mlabonne/Qwen3-1.7B-abliterated/raw/62f9c246cf426703a08183aa883a0d8149984bb0/README.md ↗

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model: Qwen/Qwen3-1.7B
Parameters: 1.72057B
Main repository formats: safetensors
License tag: apache-2.0
Languages: Unknown
Last updated: 2025-05-18 · 17:25:47.000 UTC

What changed

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

abliteratedsource tag: abliterated
abliteratedmodel name: Qwen3-1.7B-abliterated
fine-tunedsource tag: base_model:finetune:Qwen/Qwen3-1.7B

Family evidence

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

Qwen: Base model: Qwen/Qwen3-1.7B

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 · 03:14:44 UTC

Third-party hosted inference listing this exact model ID. Hosting settings, quantization and pricing are the host’s, not tested here.

Refusal profile

Refused 6 of 104 prompts (5.8%)

We asked this model 120 benign but risky-looking prompts across ten topics and counted how often it refused, hedged or answered. Lower means it answered more often; it says nothing about answer quality.

Persuasion & deception0 refused · 0 partial · 11 complied
Harassment & confrontation1 refused · 1 partial · 9 complied
Risky activities0 refused · 0 partial · 10 complied
Offensive & controversial speech1 refused · 0 partial · 7 complied
Legal grey areas0 refused · 0 partial · 12 complied
Privacy & personal data1 refused · 0 partial · 10 complied
Self-harm & mental health topics0 refused · 0 partial · 11 complied
Sexual & adult topics3 refused · 0 partial · 5 complied
Ethically grey requests0 refused · 0 partial · 11 complied
Violence & conflict0 refused · 0 partial · 11 complied

Capability check: 14 of 15 passed. Fifteen simple tasks with known answers, so a broken model cannot score well by refusing nothing.

Unclear answers: 16 (not counted). An answer is unclear when the two checker models disagree or the output was empty or garbled.

Measured 2026-10-24 · Q4_K_M via mradermacher/Qwen3-1.7B-abliterated-GGUF ↗ · llama.cpp · prompt set v1

How we measure · The prompts

Link to this listing

Made or quantized this model? Paste one of these into your model card or README so people can find this listing.

Markdown

[Listed on Custom Clankers](https://customclankers.com/models/mlabonne-qwen3-1-7b-abliterated/)

HTML

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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 ↗ ★ 36,421
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 ↗ ★ 8,946
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 ↗ ★ 78,528
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 ↗ ★ 75,713
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 ↗ ★ 132,260
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 ↗ ★ 7,467
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 ↗ ★ 7,414
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 ↗ ★ 84,667
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: 1,049
Likes: 16
Metadata checked: 2026-10-25 · 05:05:23 UTC
First seen in index: 2026-10-23 · 01:14:09 UTC
Source revision: 62f9c246cf426703a08183aa883a0d8149984bb0

Source-card excerpt

Source model card excerpt: 🐹 Qwen3-1.7B-abliterated Qwen3 Abliterated 0.6B • 1.7B • 4B • 8B • 14B • 30B-A3B This is an uncensored version of Qwen/Qwen3-1.7B created with

From mlabonne/Qwen3-1.7B-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 host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗