minicpm5-1b

• Context Length: 131072

• Model Name: minicpm5-1b

• Languages: en, zh

• Abilities: chat, reasoning, hybrid, tools

• Description: MiniCPM5-1B is the first model in the MiniCPM5 series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. Supports hybrid thinking via enable_thinking and native XML-style tool calling (MCP-compatible).

Specifications

Model Spec 1 (pytorch, 1 Billion)

• Model Format: pytorch

• Model Size (in billions): 1

• Quantizations: none

• Engines: vLLM, Transformers, SGLang

• Model ID: openbmb/MiniCPM5-1B

• 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 minicpm5-1b --size-in-billions 1 --model-format pytorch --quantization ${quantization}

Model Spec 2 (ggufv2, 1 Billion)

• Model Format: ggufv2

• Model Size (in billions): 1

• Quantizations: F16, Q4_K_M, Q8_0

• Engines: vLLM, llama.cpp

• Model ID: openbmb/MiniCPM5-1B-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 minicpm5-1b --size-in-billions 1 --model-format ggufv2 --quantization ${quantization}

Model Spec 3 (mlx, 1 Billion)

• Model Format: mlx

• Model Size (in billions): 1

• Quantizations: 4bit

• Engines: MLX

• Model ID: openbmb/MiniCPM5-1B-MLX

• 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 minicpm5-1b --size-in-billions 1 --model-format mlx --quantization ${quantization}