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dealignai/GPT-OSS-120B-MLX-CRACK

🤗 On Hugging Facetext-generationapache-2.0117B params234 GBsafetensors✓ HF checksums availableupdated today
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GPT OSS 120B — CRACK Abliterated (MLX)

OpenAI's open-weight 120B MoE model with refusal behavior removed via CRACK surgery.

Overview

This is openai/gpt-oss-120b with safety refusal behavior removed using the CRACK (Controlled Refusal Ablation via Calibrated Knockout) method. The model retains full intelligence, reasoning ability, and Harmony chain-of-thought while responding to all requests without refusal.

  • Architecture: 36-layer Mixture-of-Experts (128 experts, 4 active per token)
  • Parameters: ~117B total, ~5.7B active per token
  • Format: Native MLX — BF16 attention + mxfp4 experts (as trained by OpenAI)
  • Size: 61 GB
  • Speed: ~80 tok/s on M3 Ultra 256GB
Note on precision: GPT OSS was trained natively in mxfp4 for expert weights. This is NOT post-training quantization — the experts were trained directly in 4-bit microscaling format. There is no higher-precision version. This is the native precision as released by OpenAI.

Test Results

Evaluated across 5 independent trials (1 greedy + 4 sampled at temp=0.6) with 32 prompts per trial:

| Category | Result |

|----------|--------|

| Compliance (12 harmful prompts) | ✅ 11.8/12 average (4/5 trials perfect 12/12) |

| Coherence (20 diverse prompts) | ✅ 19.0/20 average (2/5 trials perfect 20/20) |

| Factual accuracy | ✅ Correct (geography, science, math, history) |

| Code generation | ✅ Working Python, algorithms, data structures |

| Creative writing | ✅ Poetry, stories, recipes, summaries |

| Technical explanation | ✅ Physics, biology, computing, economics |

Thinking Depth Validation

| Complexity | Greedy | Sampled |

|-----------|--------|---------|

| Simple factual | ✅ 5/5 | ✅ 10/10 |

| Multi-step reasoning | ✅ 5/5 | ✅ 9/10 |

| Complex creative/analytical | ✅ 4/5 | ✅ 8/10 |

Overall: 91% pass rate across 45 thinking-depth tests at 3 temperatures.

What CRACK does

CRACK (Controlled Refusal Ablation via Calibrated Knockout) is a weight-level intervention. No fine-tuning, no LoRA, no system prompts, no runtime hooks — the modification is permanently baked into the published weights.

The result on this model: refusal behavior is removed while the Harmony chain-of-thought channel-switching mechanism, factual recall, code generation, and creative output are all preserved (see Test Results above).

Recommended Settings

# Best results at greedy (temp=0) or warm (temp≥0.8)
# Moderate temperatures (0.5-0.7) occasionally produce channel loops on short creative prompts
temperature = 0.0  # or 0.8+

Usage

import mlx_lm
from mlx_lm.sample_utils import make_sampler

model, tokenizer = mlx_lm.load("dealignai/GPT-OSS-120B-MLX-CRACK")

prompt = "<|start|>user<|message|>Your prompt here<|end|><|start|>assistant<|message|>"

# Greedy (most reliable)
response = mlx_lm.generate(model, tokenizer, prompt=prompt, max_tokens=2048)

# Or with sampling
sampler = make_sampler(temp=0.8, top_p=0.95)
response = mlx_lm.generate(model, tokenizer, prompt=prompt, max_tokens=2048, sampler=sampler)
print(response)

Requirements

  • Apple Silicon Mac with 64GB+ unified memory (model is 61 GB)
  • Python 3.10+
  • mlx-lm >= 0.30.0

Architecture Details

GPT OSS 120B is a unique MoE architecture with:

  • 128 mxfp4 experts with 4 active per token (SwiGLU activation with hard clamp)
  • Alternating sliding/full attention (64 GQA heads, 8 KV heads, 128-token sliding window)
  • Harmony chat format with analysis/commentary/final channels for structured chain-of-thought
  • Per-head attention sinks for persistent token attention
  • YaRN RoPE for 128K context window

Other Models by dealignai

Qwen 3.5 CRACK Series

| Model | Sizes |

|-------|-------|

| Qwen 3.5 VL (Vision+Language) | 0.8B · 2B · 4B · 9B · 35B · 122B |

| Qwen 3.5 397B REAP CRACK | Text · VL |

MiniMax M2.5 CRACK Series

| Model | Quants |

|-------|--------|

| MiniMax 172B REAP CRACK | Q4 · Q6 · Q8 |

| MiniMax 139B REAP CRACK | Q4 · Q6 · Q8 |

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About dealignai

We research and publish abliterated models to advance AI safety understanding.

Follow us: 𝕏 @dealignai

See our research: Safety Generalization in Frontier MoE Models

This model is provided for research and educational purposes. Users are responsible for compliance with applicable laws and regulations. The creators are not responsible for misuse.