MiniMax-M2.7-JANGTQ_K

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MiniMax-M2.7-JANGTQ_K

MiniMax M2.7 — 74 GB on disk (down from ~230 GB FP8 source) — mixed-bit JANGTQ_K quantization in JANGTQ-PRESTACK layout.

• Source: MiniMaxAI/MiniMax-M2.7 (62 layers, 256 routed experts top-8, 196K context)
• Quantization: mixed-bit MXTQ on routed experts:
  • down_proj: 4-bit (output enters residual stream, more sensitive)
  • gate_proj: 2-bit (gated activation, less sensitive)
  • up_proj: 2-bit (gated activation)
  • attention / shared expert / embed / lm_head: 8-bit affine
  • norms / router gate / expert_bias: fp16 / fp32 passthrough
• Routed-expert layout: pre-stacked along axis 0 per the JANGTQ-PRESTACK STANDARD — instant cold load, no runtime sidecar.
• Bundle size: ~74 GB on-disk (~3-bit avg routed)
• Runs on: M3 Max 96 GB+ / M4 Max 128 GB / M5 Max 128 GB / Mac Studio

Benchmarks

MMLU eval used the standard mmlu_jangtq_resume.py runner with the model's default chat template (enable_thinking undefined → thinking ON, which the M2.7 template auto-opens with <think>\n after the assistant prefix).

Why mixed-bit?

down_proj's output enters the residual stream and accumulates across 62 layers — quantization noise compounds. gate_proj and up_proj enter through SwiGLU's multiplicative gate (silu(gate) × up) which dampens noise. Spending 4 bits on down and 2 bits on gate/up gives quality close to full-4-bit (~115 GB) at 64% the size.

Variants in the MiniMax-M2.7 line

Loading

pip install jang-tools mlx-lm

from jang_tools.load_jangtq import load_jangtq_model
model, tokenizer = load_jangtq_model("OsaurusAI/MiniMax-M2.7-JANGTQ_K")

Reasoning + tools

• Default: thinking ON (chat template inserts <think>\n after assistant prefix)
• Disable reasoning:

  messages = [{"role": "user", "content": "..."}]
  inp = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False)
  

• Reasoning parser: qwen3 (extracts <think>...</think> blocks)
• Tool parser: minimax

The chat template ships with the enable_thinking switch correctly wired both as a standalone chat_template.jinja AND inlined into tokenizer_config.json["chat_template"] for engines that read inline (vMLX, Swift swift-transformers).

Credits

• Quantization + MLX runtime: Jinho Jang ([email protected])
• Base model: MiniMaxAI — M2.7 architecture