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Model: wangzhang/gpt-oss-20b-abliterated-GGUF Source: Original Platform
2026-05-12 07:09:37 +08:00
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+---
+license: apache-2.0
+base_model: wangzhang/gpt-oss-20b-abliterated
+tags:
+  - abliterated
+  - uncensored
+  - moe
+  - gpt-oss
+  - gguf
+  - llama.cpp
+  - abliterix
+language:
+  - en
+  - zh
+library_name: llama.cpp
+pipeline_tag: text-generation
+---
+
+# gpt-oss-20b-abliterated — GGUF quants
+
+GGUF builds of [`wangzhang/gpt-oss-20b-abliterated`](https://huggingface.co/wangzhang/gpt-oss-20b-abliterated) for running in [llama.cpp](https://github.com/ggerganov/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](https://huggingface.co/wangzhang/gpt-oss-20b-abliterated) 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`](https://huggingface.co/openai/gpt-oss-20b) — base model
+- [`ggerganov/llama.cpp`](https://github.com/ggerganov/llama.cpp) — GGUF format and quantisation kernels
+- [`abliterix`](https://github.com/wuwangzhang1216/abliterix) — abliteration pipeline (Heretic derivative)