Ling-2.6-flash-JANGTQ2-CRACK

Important: This model uses the JANGTQ (JANG TurboQuant) quantization format — an extreme-compression variant of JANG for MLX on Apple Silicon that uses codebook + Hadamard rotation on routed MoE experts while keeping attention, shared expert, dense MLP, embed and lm_head at affine 8-bit. Currently only supported by MLX Studio and the jang-tools Python package. Follow @dealignai for new releases.

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Ling 2.6 Flash — JANGTQ2 + CRACK

JANGTQ TurboQuant mixed-precision | CRACK abliterated | Hybrid MLA + Linear-Attn MoE | EN + ZH | 29 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. JANGTQ2 quantized — JANGTQ2 profile (8-bit affine on attention, shared expert, dense MLP, embed and lm_head; 2-bit TurboQuant on routed experts with codebook + Hadamard rotation) — 29 GB
2. CRACK abliterated — permanent weight-level removal of safety refusal

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MMLU-200 Results (thinking OFF)

JANGTQ2 + CRACK actually edges past the un-cracked MXFP4 base on the same 200 questions. Q2 quantization noise absorbs the small directional damage from CRACK and the lower no-match count helps too.

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HarmBench-320 Results

Perfect 320/320 comply with zero EMPTY verdicts.

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Ling 2.6 Flash CRACK Series

JANGTQ2 is less than half the size of the MXFP4 variant, scores higher on both benchmarks, and is the recommended drop-in for most users.

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Usage

from jang_tools.load_jangtq import load_jangtq_model

model, tokenizer = load_jangtq_model("dealignai/Ling-2.6-flash-JANGTQ2-CRACK")

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

from mlx_lm import generate
print(generate(model, tokenizer, prompt=prompt, max_tokens=400, verbose=True))

jang-tools is required to load JANGTQ models in Python; see JANGTQ docs. For one-click runtime use MLX Studio.

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About JANGTQ

JANGTQ (JANG TurboQuant) is an extreme-compression variant of JANG that replaces affine quantization on routed MoE experts with codebook quantization + random Hadamard rotation. Precision-critical paths (attention, shared expert, embed, lm_head) stay at affine 8-bit; routed experts use packed codebook indices with tiny Lloyd-Max codebooks per layer, fused dequant + matmul Metal kernels.

For Ling 2.6 Flash, JANGTQ2 brings the model to 29 GB while preserving full bilingual capability — smallest Ling 2.6 Flash variant that maintains coherence and tool use.

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 without touching the TurboQuant codebook on routed experts. Multilingual (EN + ZH) refusal direction extraction means the model complies on both English and Chinese prompts.

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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.