Important: This model uses the JANG quantization format -- the GGUF equivalent for MLX on Apple Silicon. Currently only supported by MLX Studio and the jang-tools Python package.

MiniMax M2.5 -- JANG_4M + CRACK

JANG mixed-precision | CRACK abliterated | No guardrails | 115 GB

What Is This?

This is MiniMax M2.5 -- a 230B parameter Mixture-of-Experts model with 256 experts (8 active per token), all standard attention (no SSM), and trained with chain-of-thought reasoning.

It has been:

1. JANG quantized -- JANG_4M profile (8-bit attention, 4-bit experts) -- 115 GB
2. CRACK abliterated -- permanent weight-level removal of safety refusal

MMLU-200 Results (Thinking ON)

JANG CRACK Series Comparison

vs MLX Uniform Quantization

MLX uniform quantization is completely broken on MiniMax at ALL bit levels (~25% = random chance). JANG is the only working quantization format for this model.

HarmBench Results

295/320 (92.2%)

Install & Usage

pip install "jang[mlx]"

from jang_tools import load_for_inference
from mlx_lm import generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load_for_inference("dealignai/MiniMax-M2.5-JANG_4M-CRACK")
sampler = make_sampler(temp=1.0)  # MiniMax requires temp=1.0 for chat

messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False)

response = generate(model, tokenizer, prompt=prompt, max_tokens=2000, sampler=sampler)
print(response)

Disable Thinking (direct answers)

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False,
    enable_thinking=False)

Note: MiniMax generates a <think> chain before answering by default. Use max_tokens=2000+ for complex questions. For chat, use temperature=1.0 (greedy causes loops).

About JANG

JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon -- the GGUF equivalent for MLX. Classifies tensors into sensitivity tiers and assigns bits accordingly.

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety alignment from LLMs at the weight level. This model has been abliterated using proprietary techniques achieving full compliance while preserving reasoning quality.

Links

Disclaimer

This model is provided for research and educational purposes. The creators are not responsible for any misuse. By downloading this model, you agree to use it responsibly and in compliance with applicable laws.