Instructions to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

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

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dealignai/Ling-2.6-flash-JANGTQ2-CRACK" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "dealignai/Ling-2.6-flash-JANGTQ2-CRACK",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dealignai/Ling-2.6-flash-JANGTQ2-CRACK

Run Hermes

hermes

Ling-2.6-flash-JANGTQ2-CRACK / README.md

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metadata

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 and the jang-tools Python package. Follow @dealignai for new releases.

MLX Studio — the only app that natively supports JANG / JANGTQ models

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}

Ling 2.6 Flash — JANGTQ2 + CRACK

JANGTQ TurboQuant mixed-precision | CRACK abliterated | Hybrid MLA + Linear-Attn MoE | EN + ZH | 29 GB

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:

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
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	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	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

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.

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 — check out the Ko-fi membership for early access and extras.

dealign.ai

Twitter · HF · Ko-fi

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.