Instructions to use JANGQ-AI/MiniMax-M2.7-JANG_6M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/MiniMax-M2.7-JANG_6M 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("JANGQ-AI/MiniMax-M2.7-JANG_6M")

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 JANGQ-AI/MiniMax-M2.7-JANG_6M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/MiniMax-M2.7-JANG_6M"

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": "JANGQ-AI/MiniMax-M2.7-JANG_6M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JANGQ-AI/MiniMax-M2.7-JANG_6M 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 "JANGQ-AI/MiniMax-M2.7-JANG_6M"

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 JANGQ-AI/MiniMax-M2.7-JANG_6M

Run Hermes

hermes

OpenClaw new

How to use JANGQ-AI/MiniMax-M2.7-JANG_6M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/MiniMax-M2.7-JANG_6M"

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 "JANGQ-AI/MiniMax-M2.7-JANG_6M" \
  --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 JANGQ-AI/MiniMax-M2.7-JANG_6M with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "JANGQ-AI/MiniMax-M2.7-JANG_6M"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "JANGQ-AI/MiniMax-M2.7-JANG_6M"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "JANGQ-AI/MiniMax-M2.7-JANG_6M",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniMax-M2.7-JANG_6M / README.md

jangq

Add tool-use guidance for interleaved thinking clients

c53b5f8 verified 3 months ago

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	---
	license: other
	license_name: minimax-open
	library_name: mlx
	tags:
	- mlx
	- jang
	- minimax
	- moe
	- apple-silicon
	pipeline_tag: text-generation
	---

	<p align="center">
	<img src="mlx-studio-logo.png" alt="MLX Studio" width="400"/>
	</p>

	<p align="center">
	<img src="jangq-logo.png" alt="JANGQ" width="200"/>
	</p>

	<div align="center">

	# 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.
	</div>

	> Recommended: Run in [MLX Studio](https://mlxstudio.com) 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](https://huggingface.co/JANGQ-AI/MiniMax-M2.7-JANG_2L) \| (8, 6, 2) \| 63 GB \| 2.10 \|
	\| [JANG_3L](https://huggingface.co/JANGQ-AI/MiniMax-M2.7-JANG_3L) \| (8, 4, 3) \| 89 GB \| 3.08 \|
	\| [JANG_4M](https://huggingface.co/JANGQ-AI/MiniMax-M2.7-JANG_4M) \| (8, 4, 4) \| 115 GB \| 4.06 \|
	\| [JANG_6M](https://huggingface.co/JANGQ-AI/MiniMax-M2.7-JANG_6M) \| (8, 6, 6) \| 167 GB \| 6.03 \|

	## Requirements

	- Apple Silicon Mac with 192 GB unified memory
	- MLX framework
	- [MLX Studio](https://mlxstudio.com) 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:

	```python
	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:
	```xml
	<minimax:tool_call>
	<invoke name="tool_name">
	<parameter name="param1">value1</parameter>
	</invoke>
	</minimax:tool_call>
	```

	## Usage

	```python
	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](https://mlxstudio.com) \| [JANGQ](https://jangq.ai) \| [X @dealignai](https://x.com/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.