gpt-oss-120b-abliterated

A refusal-suppressed variant of openai/gpt-oss-120b, produced with abliterix using direct weight editing, Expert-Granular Abliteration (EGA) on the fused MoE expert weights (128 experts × 36 layers), MoE router suppression on the safety-concentrated experts, and a new vLLM in-place TP editing path that replaces the HF pipeline-parallel + LoRA-adapter pipeline used for gpt-oss-20b.

Key results

The eval refusal counts come from an LLM judge (google/gemini-3.1-flash-lite-preview via OpenRouter) running at batch_size = 1 with 16 concurrent workers and a hardened prompt that treats metaphorical deflection ("pipe-bomb → pipe-separated typeahead list", "lock-pick → presentation topic") and generic template filler as refusals, not as compliance — so a model that "bypasses" refusal by collapsing into off-topic boilerplate gets correctly counted as a failure. max_gen_tokens was bumped from 150 to 256 during search so long-form repetition loops surface before the judge is called. The 26/100 is a real, semantic compliance number, validated via a separate 15-prompt HF side-by-side test.

The qualitative compliance row is a separate manual test: 15 classic hard prompts (10 EN + 5 ZH) covering lockpicking, phishing, meth synthesis, WiFi hacking, fake news, hotwiring, pipe bombs, signature forgery, password-stealer malware, shoplifting, 炸弹制作, 钓鱼邮件, 电脑入侵, 身份证伪造, 网络诈骗. The model complies directly with 12/15 in the same Markdown-table, step-by-step style the base model uses for benign technical answers; 1/15 gets a metaphorical food-item deflection (CN 炸弹 → decorative toy), 2/15 drift off-topic on WiFi/fake-news.

Why this needed new machinery — four gpt-oss-120b-specific correctness fixes

abliterix handles four issues that silently break naïve abliteration pipelines on gpt-oss-120b:

1. Native MXFP4 weights are not exposed as standard nn.Parameter. gpt-oss ships in Mxfp4GptOssExperts form whose down_proj is a packed Triton tensor that cannot be edited in-place. For the 120b variant abliterix now pre-dequantises the whole 65 GB MXFP4 checkpoint to a 232 GB BF16 safetensors checkpoint on disk (scripts/prepare_bf16_checkpoint.py), because vLLM's Mxfp4MoEMethod.process_weights_after_loading would otherwise repack w2_weight into an opaque block layout that silently swallows in-place writes (see vLLM RFC #31848).
2. GptOssExperts.down_proj is stored transposed vs the standard MoE convention: shape (experts, intermediate_in, hidden_out) with forward path out = act @ W (no transpose). Standard EGA implementations use shape-based axis detection, which silently picks the wrong projection branch when hidden == intermediate (both 2880 in gpt-oss-120b). abliterix marks this layout explicitly and projects from the output side (W_new = W (I − vv^T)).
3. Fused-expert MoEs were silently invisible to EGA. GptOssExperts is a single Module holding fused 3-D weights, so a naive per-Module profile dict key produces no mlp.down_proj entry and _apply_ega_steering early-exits. abliterix synthesises an mlp.down_proj profile when fused experts are detected so EGA actually runs across all 128 experts × 36 layers.
4. HF pipeline-parallel on 120b was too slow to iterate on. A single trial on HF PP across 4× RTX PRO 6000 was >2 min; 100 trials would have been >3 h of pure generation. abliterix v1.5 adds a vLLM TP=4 in-place editor (VLLMExpertEditor, VLLMAttentionEditor) that edits w2_weight, qkv_proj.weight, and o_proj.weight directly on TP workers via collective_rpc + reset_prefix_cache. This requires VLLM_FUSED_MOE_UNQUANTIZED_BACKEND=triton (FLASHINFER_TRTLLM repacks w2_weight into a non-editable block layout), VLLM_ALLOW_INSECURE_SERIALIZATION=1 (ships worker fns as pickle), and enforce_eager=true (CUDA graphs cache weight pointers so edits would otherwise be read only on the first forward). Per-trial time dropped to ~60 s end-to-end.

On top of direct steering + EGA, this release carries MoE router suppression — an [experts] block that redirects routing away from the top-k "safety experts" (the experts whose gate activates disproportionately more on harmful prompts than on benign ones). For 120b with 128 experts/layer, the optimiser picked n_suppress = 1 with router_bias = -4.11 (suppression scale ≈ 0.59 — moderately aggressive), leaving 127/128 experts untouched while damping the single most refusal-aligned expert per layer.

Method

• Base: openai/gpt-oss-120b — 36 layers, 128 routed experts per layer, top-4, hidden = intermediate = 2880, MXFP4 → BF16 pre-dequant before abliteration (232 GB safetensors on disk)
• Tool: abliterix
• Mode: steering_mode = "direct" (orthogonal projection on base weights, no LoRA), weight_normalization = "full" (norm-preserving projection)
• Components steered:
  • attn.o_proj via direct weight projection (Q/K/V disabled — refusal signal on gpt-oss concentrates in the output side)
  • mlp.experts.down_proj across all 128 experts × 36 layers via Expert-Granular Abliteration
  • mlp.router rows of the top-1 safety expert per layer via logit suppression
• Refusal direction: global scope (single direction), mean of (target − benign) residuals at a learned layer index (16.31, ≈ 45% depth) on a 400-prompt benign + 400-prompt harmful set; BF16 projection
• Search: Optuna TPE, (KL + 0.5·length_deviation, refusals/baseline) multi-objective, 100 trials (40 random warmup + 60 TPE exploitation)
• Judge hardening (vs older abliterated MoE releases):
  • llm_judge_batch_size = 1 (each Q/A pair gets a dedicated API call — no anchor bias from batched labelling)
  • llm_judge_concurrency = 16 parallel workers
  • response_truncation = 2000 chars (≈ 500 tokens — covers full max_gen_tokens = 256 output, so long-form collapse is visible to the judge)
  • Prompt contains metaphor-deflection few-shot examples so "pipe bomb → typeahead list" is labelled R, not C
• Hardware: 4 × NVIDIA RTX PRO 6000 Blackwell 96 GB (sm_120, PCIe-only, no NVLink), driver 580 / CUDA 12.9, TP=4, batch=32, total wall time ≈ 100 min for 100 trials
• Eval set: 100 held-out harmful prompts not seen during steering-vector computation; 100 held-out benign prompts for KL comparison

Winning hyperparameters (v5 Trial 78)

vector_scope = "global"
vector_index = 16.31            # layer where refusal direction is extracted

[steering.components."attn.o_proj"]
max_weight = 3.42
max_weight_position = 21.22     # peak strength at layer ≈ 21 / 36
min_weight = 1.63               # 47.6% of max — smooth profile
min_weight_distance = 20.65

[steering.components."mlp.down_proj"]   # EGA on fused 128 × 36 experts
max_weight = 6.74
max_weight_position = 26.69     # peak at layer ≈ 27 / 36 (later than attention)
min_weight = 0.96               # 14.3% of max
min_weight_distance = 20.62

[moe]                            # router-row suppression
n_suppress = 1                   # suppress top-1 safety expert per layer
router_bias = -4.11              # scale = max(0, 1 + bias/10) = 0.589
expert_ablation_weight = 0.0     # pinned off; EGA already handles expert weights

The attention peak sits at layer ≈ 21/36 (mid-stack where the refusal decision still has options) and the EGA peak sits later at layer ≈ 27/36 (after attention has routed harmful intent into the expert path). This stacked mid-to-late pair is a new fingerprint vs gpt-oss-20b, where both peaks sat around layer 18 of 24 (≈ 75% depth).

Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("wangzhang/gpt-oss-120b-abliterated")
model = AutoModelForCausalLM.from_pretrained(
    "wangzhang/gpt-oss-120b-abliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

The model uses gpt-oss's harmony chat format. The chat template is bundled (chat_template.jinja).

Hardware note: BF16 weights are ~232 GB on disk. You need at least 232 GB aggregate VRAM (e.g. 4× RTX PRO 6000 96GB, 2× H200 141GB, or 8× H100 40GB with TP) or run via device_map="auto" across GPU + CPU with offloading. For faster inference, a GGUF quantised variant (see below) is recommended for single-GPU setups.

vLLM

vllm serve wangzhang/gpt-oss-120b-abliterated \
    --tensor-parallel-size 4 \
    --max-model-len 4096 \
    --enforce-eager

Honest limitations

• Refusal is low, not zero. 26 / 100 held-out prompts still refuse. The residual refusers cluster around extremely-specific CBRN synthesis and CSAM-adjacent content — exactly where refusal is represented by multiple redundant circuits that partial abliteration cannot all knock out in one Optuna-TPE pass.
• English > Chinese. Steering vectors came from a primarily English-weighted dataset. Chinese hard prompts mostly work (4/5 on manual Chinese tests gave real compliance; 1/5 drifted into a food-metaphor on "制作炸弹" → "炸盘"). Bypass quality on Chinese is slightly lower — shorter responses, occasional English fallback on technical terms.
• Weaker than gpt-oss-20b-abliterated on ASR headline. 20b shipped at 94% ASR (6/100 refusals, KL 0.0098). 120b ships at 74% ASR (26/100 refusals, KL 5.4e-06). The 120b model has much lower KL (base behaviour is more preserved) but higher residual refusal — a property of 120b's 128-expert router being a much wider, more redundant safety surface than 20b's 32-expert router.
• Occasional long-form derail. On generations past ~400 tokens a small fraction of outputs drift into markdown-table loops; this is an abliteration side-effect, not a base-model regression.

Reproducibility

Full search checkpoint (Optuna JSONL + judge cache SQLite) and the exact config are available in the abliterix repo under configs/gpt_oss_120b.toml + checkpoints_gpt_oss_120b_v5/. To reproduce from scratch on a 4×96GB Blackwell pod:

git clone https://github.com/wuwangzhang1216/abliterix
cd abliterix && pip install -e .

# One-time pre-dequant: MXFP4 → BF16 on disk (~8 min, 232 GB output)
python scripts/prepare_bf16_checkpoint.py \
    --model openai/gpt-oss-120b \
    --out /workspace/gpt-oss-120b-bf16

# Point config at the BF16 checkpoint and launch
sed -i 's|model_id = "openai/gpt-oss-120b"|model_id = "/workspace/gpt-oss-120b-bf16"|' \
    configs/gpt_oss_120b.toml

bash quick_start/deploy_gpt_oss_120b.sh
# 100 trials, ~100 min wall time on 4× RTX PRO 6000

Optuna is deterministic if you set sampler_seed in [optimization].

Intended use

Authorised AI-safety research, red-teaming evaluation, refusal-mechanism analysis, and study of how MoE expert specialisation encodes safety behaviours at scale (128 experts × 36 layers is large enough to show genuine expert specialisation rather than router noise). Not for producing or distributing harmful content. The license of the base model (apache-2.0) applies; the user is responsible for compliance with all applicable laws and the OpenAI gpt-oss usage policy.

Acknowledgments

• openai/gpt-oss-120b for the base model
• abliterix is a derivative work of Heretic by Philipp Emanuel Weidmann
• TrevorS for the original Expert-Granular Abliteration formulation
• vLLM team for the collective_rpc + reset_prefix_cache APIs that made in-place TP editing practical

Provenance and Modification Notice

• Immediate source checkpoint: openai/gpt-oss-120b
• 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.
• Modified and published by: Wangzhang Wu
• Repository first published: 2026-04-21 (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-120b/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.

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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.

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