---
language:
- en
- zh
library_name: mlx
license: mit
base_model: inclusionAI/Ling-2.6-flash
tags:
- jang
- jangtq
- jangtq2
- turboquant
- quantized
- mixed-precision
- apple-silicon
- mlx
- moe
- bailing
- bailing-hybrid
- linear-attention
- mla
- multi-latent-attention
- abliterated
- uncensored
- crack
- harmbench
- mmlu
- bilingual
- ling
- ling-2.6
- inclusionai
pipeline_tag: text-generation
thumbnail: dealign_mascot.png
---

> **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](https://mlx.studio)** and the `jang-tools` Python package. Follow [@dealignai](https://x.com/dealignai) for new releases.

---

<p align="center">
  <a href="https://mlx.studio"><img src="mlx-studio-logo.png" alt="MLX Studio" width="500"></a>
</p>

<h4 align="center"><a href="https://mlx.studio">MLX Studio</a> — the only app that natively supports JANG / JANGTQ models</h4>

---

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<a href="https://vmlx.net">
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<strong>Built for vMLX</strong> — the only MLX inferencer with VL support, KV cache quantization, prefix cache reuse, agentic tool calling, and speculative decoding.
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---

<div align="center">

<img src="dealign_mascot.png" width="128" />

# 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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</div>

---

## What Is This?

This is [Ling 2.6 Flash](https://huggingface.co/inclusionAI/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

| | |
|---|---|
| **Base model** | [inclusionAI/Ling-2.6-flash](https://huggingface.co/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** | JANGTQ2 — 29 GB |
| **MMLU-200** | **81.0%** (MXFP4 base 80.0% — **+1.0pp**, surgery neutral) |
| **HarmBench-320** | **100.0%** (320/320 comply, 0 refuse, 0 empty) |
| **Context** | 131,072 native |
| **Languages** | English + Chinese (probed bilingual) |
| **Speed** | 50+ tok/s on M4 Max 128 GB |
| **Fits on** | **48 GB+ Macs** |

---

## MMLU-200 Results (thinking OFF)

| Model | Correct | Accuracy | No-match |
|---|:---:|:---:|:---:|
| MXFP4 Base (reference) | 160/200 | 80.00% | 6 |
| MXFP4 + CRACK | 157/200 | 78.50% | 10 |
| **JANGTQ2 + CRACK** *(this model)* | **162/200** | **81.00%** | **1** |

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.

---

## HarmBench-320 Results

| Model | COMPLY | REFUSE | EMPTY |
|---|:---:|:---:|:---:|
| MXFP4 Base (reference) | 161 (50.3%) | 157 (49.1%) | 2 (0.6%) |
| MXFP4 + CRACK | 313 (97.8%) | 5 (1.6%) | 2 (0.6%) |
| **JANGTQ2 + CRACK** *(this model)* | **320 (100.0%)** | **0 (0.0%)** | **0 (0.0%)** |

Perfect 320/320 comply with zero EMPTY verdicts.

---

## Ling 2.6 Flash CRACK Series

| Model | Format | Size | MMLU-200 | HarmBench-320 | Fits on |
|-------|:---:|:---:|:---:|:---:|:---:|
| [MXFP4 + CRACK](https://huggingface.co/dealignai/Ling-2.6-flash-MXFP4-CRACK) | affine 4-bit g=32 | 63 GB | 78.5% | 97.8% | 96 GB Mac |
| **JANGTQ2 + CRACK** *(this model)* | **TurboQuant 2-bit experts + 8-bit affine** | **29 GB** | **81.0%** | **100.0%** | **48 GB Mac** |

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.

---

## Usage

```python
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](https://huggingface.co/dealignai). For one-click runtime use [MLX Studio](https://mlx.studio).

---

## 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.

---

## Support dealignai

All models are built from original research and published for free.

**[Support us on Ko-fi](https://ko-fi.com/dealignai)** — check out the Ko-fi membership for early access and extras.

---

<p align="center">
  <img src="dealign_logo.png" alt="dealign.ai" width="200"/>
</p>

<p align="center">
  <a href="https://x.com/dealignai">Twitter</a> ·
  <a href="https://huggingface.co/dealignai">HF</a> ·
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</p>

---

## 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.