MiniMax-M2.7 JANG_6M
MiniMax M2.7 228B MoE — 6.03-bit mixed precision, 167 GB
Near-lossless quantization for maximum quality on Apple Silicon.
Recommended: Run in MLX Studio for best experience including thinking mode support and optimized MoE inference.
Important Settings
MiniMax M2.7 is an always-reasoning model. It thinks before answering on every prompt.
| Setting | Value | Notes |
|---|---|---|
| Temperature | 1.0 | REQUIRED — greedy/temp=0 causes infinite thinking loops |
| Top P | 0.95 | |
| Top K | 40 | |
| Repetition Penalty | 1.1 | Optional, helps prevent loops |
Model Details
| Metric | Value |
|---|---|
| Source | MiniMaxAI/MiniMax-M2.7 (FP8 E4M3) |
| Architecture | MoE (256 experts, top-8 active), GQA (48 heads / 8 KV), partial RoPE |
| Total Parameters | 228.7B |
| Active Parameters | ~1.4B per token |
| Profile | JANG_6M (CRITICAL=8-bit, IMPORTANT=6-bit, COMPRESS=6-bit) |
| Actual avg bits | 6.03 |
| Model size | 167 GB |
| Format | JANG v2 (MLX-native safetensors, instant load) |
| group_size | 128 (speed-optimized for 256 experts) |
| Routing | Sigmoid + bias correction (not softmax) |
| QK-norm | Full vector RMSNorm |
| Context | 192K tokens |
JANG_6M Bit Allocation
| Tier | Components | Bits |
|---|---|---|
| CRITICAL | Attention (Q/K/V/O), lm_head | 8 |
| IMPORTANT | Embeddings | 6 |
| COMPRESS | Expert MLP (w1/w2/w3) — 98%+ of params | 6 |
| Passthrough | MoE router/gate (float16), norms, QK-norms | 16 |
JANG protects routing and attention at full precision while compressing the 256 expert MLPs — where MoE models are most tolerant of quantization. The router is kept at float16 (no quantization) for maximum routing precision.
MMLU Benchmarks (200q, 10 subjects, reasoning ON)
Coming soon — benchmarks in progress.
Why JANG for MiniMax
Standard MLX quantization on MiniMax produces completely broken output at ALL bit levels (~25% MMLU = random guessing). JANG's mixed-precision approach is the only working quantized MiniMax on Apple Silicon.
On M2.5, JANG_2L achieved 74% MMLU vs MLX's 25% (random). M2.7 results pending.
All Quantizations
| Model | Profile | Size | Avg Bits |
|---|---|---|---|
| JANG_2L | (8, 6, 2) | 63 GB | 2.10 |
| JANG_3L | (8, 4, 3) | 89 GB | 3.08 |
| JANG_4M | (8, 4, 4) | 115 GB | 4.06 |
| JANG_6M | (8, 6, 6) | 167 GB | 6.03 |
Requirements
- Apple Silicon Mac with 192 GB unified memory
- MLX framework
- MLX Studio recommended
Tool Use / Agent Mode
MiniMax M2.7 uses interleaved thinking + tool calls — it reasons inside <think> blocks, then emits tool calls in <minimax:tool_call> format. Some clients (Opencode, etc.) may strip the <think> block and miss the tool call.
For tool-use clients, set enable_thinking=False in the chat template:
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
enable_thinking=False # skips <think> injection for tool-use
)
MiniMax tool call format:
<minimax:tool_call>
<invoke name="tool_name">
<parameter name="param1">value1</parameter>
</invoke>
</minimax:tool_call>
Usage
from jang_tools.loader import load_jang_model
from mlx_lm import generate
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load_jang_model("JANGQ-AI/MiniMax-M2.7-JANG_6M")
sampler = make_sampler(temp=1.0, top_p=0.95)
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "What is photosynthesis?"}],
tokenize=False, add_generation_prompt=True
)
output = generate(model, tokenizer, prompt=prompt, max_tokens=2048, sampler=sampler)
print(output)
Support
MLX Studio | JANGQ | X @dealignai
Quantized by Jinho Jang (eric@jangq.ai) using JANG Tools v2.4.1.
This model is provided for research and personal use. Users are responsible for ensuring their use complies with applicable laws and the MiniMax license.