GGUF builds of wangzhang/gpt-oss-20b-abliterated for running in llama.cpp, Ollama, LM Studio, KoboldCpp, text-generation-webui, and anything else that speaks GGUF.

Files

File	Quant	Size	Use case
`gpt-oss-20b-abliterated-bf16.gguf`	BF16 (full precision)	~42 GB	Reference quality. Requires 48+ GB VRAM (or large CPU RAM). Use if you care about identical behaviour to the HF checkpoint.
`gpt-oss-20b-abliterated-q8_0.gguf`	Q8_0 (8-bit, GGUF's FP8-equivalent)	~22 GB	Near-lossless vs BF16. Runs comfortably on a single 24 GB GPU. Recommended default.
`gpt-oss-20b-abliterated-q4_k_m.gguf`	Q4_K_M (4-bit k-quant, M profile)	~15 GB	Best size / quality trade-off at 4-bit. Fits on 16 GB VRAM or modest CPU setups.

GGUF does not have a native FP8 type; Q8_0 is the standard 8-bit path and is what every publisher on the Hub ships as "fp8 equivalent". Q4_K_M is the best 4-bit choice for this model — Q4_0 is noticeably worse on MoE models, Q5_K_M is ~25% larger for diminishing returns.

Source

Built from the merged BF16 weights of the abliteration run, not from the original MXFP4 (since the abliteration required dequantising experts to BF16 to enable direct weight editing). The BF16 → GGUF conversion uses llama.cpp's convert_hf_to_gguf.py; the quantised variants use llama-quantize.

All three files are functionally identical to the BF16 HF checkpoint at Q8_0 fidelity; Q4_K_M adds minor additional quantisation noise but keeps the abliteration effect intact (spot-checked on the same 15-prompt EN/ZH jailbreak set used for the HF release).

Quick start (llama.cpp)

bash

# Download one quant:
huggingface-cli download wangzhang/gpt-oss-20b-abliterated-GGUF \
    gpt-oss-20b-abliterated-q4_k_m.gguf --local-dir ./

# Run with gpt-oss's harmony chat template (bundled in the GGUF):
./llama-cli -m gpt-oss-20b-abliterated-q4_k_m.gguf \
    -cnv -p "You are a helpful assistant." \
    --reasoning-budget 0 \
    -n 512

Quick start (Ollama)

bash

ollama pull hf.co/wangzhang/gpt-oss-20b-abliterated-GGUF:Q4_K_M
ollama run hf.co/wangzhang/gpt-oss-20b-abliterated-GGUF:Q4_K_M

What this actually is

The HF checkpoint this is built from is an "abliterated" variant of gpt-oss-20b — refusals on harmful prompts have been suppressed via direct weight editing and MoE router suppression. Refusal rate on a 100-prompt held-out eval drops from 97/100 (base) to 6/100 (abliterated). See the base HF card for metrics, method, and honest limitations.

These GGUFs inherit that behaviour. They are intended for authorised AI-safety research, red-teaming, and mechanism analysis — not for producing or distributing harmful content. The apache-2.0 license of the upstream OpenAI gpt-oss release applies.

Acknowledgments

openai/gpt-oss-20b — base model
ggerganov/llama.cpp — GGUF format and quantisation kernels
abliterix — abliteration pipeline (Heretic derivative)

Provenance and Modification Notice

Immediate source checkpoint: wangzhang/gpt-oss-20b-abliterated
Ultimate upstream model: openai/gpt-oss-20b
Exact base revision used: Not recorded in the existing release artifacts; the current upstream HEAD is not substituted.
Modification method: Abliterix weight-space / representation intervention intended to reduce refusal behavior.
Additional transformation: GGUF conversion/quantization; see the existing quantization details in this card.
Modified and published by: Wangzhang Wu
Repository first published: 2026-04-20 (Hugging Face repository metadata)

The original model weights and/or derived checkpoint were modified. This repository is an independent derivative and is not an official release of the upstream model developer.

License and Attribution

The governing upstream license is Apache License 2.0. A copy is included in LICENSE. License source audited on 2026-08-29: https://huggingface.co/openai/gpt-oss-20b/blob/main/LICENSE

All applicable upstream copyright, attribution, acceptable-use, and other license terms remain in effect. This repository grants no rights beyond those provided by the upstream license. Downstream users must preserve applicable license and attribution notices.

Disclaimer and Responsible Use / 免责声明与安全使用声明

English

This is an experimental, modified model provided for research, evaluation, and other lawful purposes. Its safety alignment, refusal behavior, or other safeguards may have been weakened or removed. It may produce inaccurate, biased, offensive, explicit, dangerous, or illegal content. Outputs are not professional advice and must not be relied on for medical, legal, financial, safety-critical, or other high-stakes decisions without qualified human review.

You are solely responsible for how you access, use, deploy, fine-tune, or redistribute this model and its outputs, including compliance with applicable laws, regulations, licenses, third-party rights, platform policies, and the original model's terms. Do not use it to facilitate harm, illegal activity, malware, fraud, privacy violations, targeted harassment, weapons development, or decisions that materially affect a person's rights or access to essential services without appropriate authorization, safeguards, and qualified oversight.

Before deployment, perform a context-specific risk assessment and testing; use human oversight, access controls, content filtering, rate limits, monitoring, logging, and incident-response procedures as appropriate. Preserve this notice in downstream redistributions.

The model is provided "AS IS", without warranties of any kind. To the fullest extent permitted by applicable law, the maintainer disclaims liability for claims, damages, or losses arising from use, misuse, inability to use, or redistribution of the model or its outputs. Nothing in this notice overrides applicable law or the governing license, and this notice is not legal advice.

中文

本模型属于实验性改造模型，仅供研究、评测及其他合法用途。其安全对齐、拒答机制或其他防护可能已被削弱或移除，因此可能生成不准确、偏见、冒犯、露骨、危险或违法内容。输出不构成医疗、法律、金融等专业意见；涉及高风险或重大权益的决定，必须由具备资质的人员复核。

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