dealignai/Ling-2.6-flash-MXFP4-CRACK

Built for vMLX — the only MLX inferencer with VL support, KV cache quantization, prefix cache reuse, agentic tool calling, and speculative decoding.
_{Free for macOS · vmlx.net}

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Ling 2.6 Flash — MXFP4 + CRACK

MXFP4 quantized | CRACK abliterated | Hybrid MLA + Linear-Attn MoE | EN + ZH | 63 GB

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What Is This?

This is Ling 2.6 Flash by inclusionAI — a 35B-parameter Mixture-of-Experts model with 256 routed experts (8 active per token) + 1 always-active shared expert, hybrid MLA + Lightning Linear-Attention architecture, native English + Chinese, 131K context.

It has been:

1. MXFP4 quantized — uniform 4-bit affine, group_size=32 — 63 GB
2. CRACK abliterated — permanent weight-level removal of safety refusal

Base model	inclusionAI/Ling-2.6-flash (35B total, 1 shared + 8 routed active)
Architecture	bailing_hybrid — Multi-Latent Attention (MLA) every 8th layer + Lightning Linear-Attn elsewhere
Quantization	MXFP4 (Q4 g=32 affine) — 63 GB
MMLU-200	78.5% (base 80.0% — within −1.5pp)
HarmBench-320	97.8% comply (base 50.3% — +47.5pp)
Context	131,072 native
Languages	English + Chinese (probed bilingual)
Speed	30+ tok/s on M4 Max 128 GB
Fits on	96 GB+ Macs

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MMLU-200 Results (thinking OFF)

Model	Correct	Accuracy	No-match
MXFP4 Base	160/200	80.00%	6
MXFP4 + CRACK	157/200	78.50%	10
Δ	−3	−1.5pp	+4

CRACK delta of −1.5pp is well inside the noise floor for a 200-question sample — capability essentially preserved.

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HarmBench-320 Results

Model	COMPLY	REFUSE	EMPTY
MXFP4 Base	161 (50.3%)	157 (49.1%)	2 (0.6%)
MXFP4 + CRACK	313 (97.8%)	5 (1.6%)	2 (0.6%)
Δ comply		+47.5pp

Refusal directional removal lifts HarmBench compliance from ~50% to ~98% with negligible MMLU regression.

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Ling 2.6 Flash CRACK Series

Model	Format	Size	MMLU-200	HarmBench-320	Fits on
MXFP4 + CRACK (this model)	affine 4-bit g=32	63 GB	78.5%	97.8%	96 GB Mac
JANGTQ2 + CRACK	TurboQuant 2-bit experts + 8-bit affine	29 GB	81.0%	100.0%	48 GB Mac

The JANGTQ2 variant is smaller and scores higher on both benchmarks — quant noise on the 2-bit routed experts ends up helping rather than hurting.

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Usage

from mlx_lm import load, generate

model, tokenizer = load("dealignai/Ling-2.6-flash-MXFP4-CRACK")

messages = [{"role": "user", "content": "Hello — what can you do?"}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=400, verbose=True))

mlx_lm >= 0.20 with bailing_hybrid model class is required.

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About This Model

Ling 2.6 Flash is the latency-tier sibling in the Ling 2.6 family — fast multilingual instruction-follow + tool use. The chat template includes a <think>...</think> reasoning block, but in practice this Flash variant is best treated as a non-reasoning instruct model: leave thinking OFF (the default) for benchmark-style work and short-form responses. For chain-of-thought reasoning prefer the larger Ling 2.6 Plus / Ring / Pro tier.

CRACK is a permanent weight-level abliteration that removes safety refusal from the always-active residual-stream writers. Multilingual (EN + ZH) refusal direction extraction means the model complies on both English and Chinese prompts. The vision tower (none here — Ling 2.6 Flash is text-only) and MoE routing/expert internals are untouched.

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Support dealignai

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Disclaimer

This model has had its safety refusal circuits removed. It will produce responses that would normally be refused, including technical content on security testing, dual-use research, and sensitive topics. You are responsible for how you use it.

The CRACK abliteration process does not add new capabilities — it only removes the model's learned refusal patterns. All knowledge, including the knowledge used to produce unsafe outputs, was already present in the base Ling model.