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_2L + CRACK

JANG mixed-precision · CRACK abliterated · No guardrails · 63 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_2L profile (8-bit attention, 6-bit embeddings, 2-bit experts) — 63 GB
2. CRACK abliterated — permanent weight-level removal of safety refusal

MMLU-200 Results

JANG CRACK vs Base vs MLX Uniform

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.

Per Subject

Safety guardrails were actively degrading the model's reasoning ability. CRACK surgery unlocked the model's full capacity for mathematical and logical reasoning.

HarmBench Results

314/320 (98.1%) — tested with enable_thinking=false, temperature=1.0

Install & Usage

pip install "jang[mlx]"

from jang_tools.loader import load_jang_model
from mlx_lm import generate

model, tokenizer = load_jang_model("dealignai/MiniMax-M2.5-JANG_2L-CRACK")

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)
print(response)

Note: MiniMax generates a <think> chain before answering by default. To disable thinking, pass enable_thinking=False in your chat template kwargs. Use max_tokens=2000+ for complex questions. For chat applications, 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 using per-layer projected vectors from structurally-mirrored prompt pairs.

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.

한국어

MiniMax M2.5 — JANG_2L + CRACK

pip install "jang[mlx]"

GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai