MiniMax-M2.7#

  • Context Length: 204800

  • Model Name: MiniMax-M2.7

  • Languages: en, zh

  • Abilities: chat, tools, reasoning, hybrid

  • Description: MiniMax-M2.7 is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search

Specifications#

Model Spec 1 (pytorch, 230 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 230

  • Quantizations: none

  • Engines: Transformers

  • Model ID: MiniMaxAI/MiniMax-M2.7

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name MiniMax-M2.7 --size-in-billions 230 --model-format pytorch --quantization ${quantization}

Model Spec 2 (ggufv2, 230 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 230

  • Quantizations: none

  • Engines:

  • Model ID: unsloth/MiniMax-M2.7-GGUF

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name MiniMax-M2.7 --size-in-billions 230 --model-format ggufv2 --quantization ${quantization}

Model Spec 3 (mlx, 230 Billion)#

  • Model Format: mlx

  • Model Size (in billions): 230

  • Quantizations: 3bit, 4bit, 5bit, 6bit, 8bit

  • Engines:

  • Model ID: mlx-community/MiniMax-M2.7-{quantization}

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name MiniMax-M2.7 --size-in-billions 230 --model-format mlx --quantization ${quantization}