minicpm4#

  • Context Length: 32768

  • Model Name: minicpm4

  • Languages: zh

  • Abilities: chat

  • Description: MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.

Specifications#

Model Spec 1 (pytorch, 0_5 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 0_5

  • Quantizations: none

  • Engines: vLLM, Transformers

  • Model ID: JunHowie/MiniCPM4-0.5B

  • 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 minicpm4 --size-in-billions 0_5 --model-format pytorch --quantization ${quantization}

Model Spec 2 (pytorch, 8 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 8

  • Quantizations: none

  • Engines: vLLM, Transformers

  • Model ID: JunHowie/MiniCPM4-8B

  • 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 minicpm4 --size-in-billions 8 --model-format pytorch --quantization ${quantization}

Model Spec 3 (mlx, 8 Billion)#

  • Model Format: mlx

  • Model Size (in billions): 8

  • Quantizations: 4bit

  • Engines: MLX

  • Model ID: mlx-community/MiniCPM4-8B-4bit

  • 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 minicpm4 --size-in-billions 8 --model-format mlx --quantization ${quantization}