How to use from
MLX LM
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abenzerps/MiniCPM5-2B-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abenzerps/MiniCPM5-2B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "abenzerps/MiniCPM5-2B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
Quick Links

MiniCPM5-2B MLX - 4-bit

MLX 4-bit conversion of OpenBMB/MiniCPM5-2B, a 2B dense Llama-based model for local deployment, coding, reasoning, long-context work, and tool use. The source checkpoint supports a native context length of 131,072 tokens (128K).

Benchmarks

MiniCPM5-2B evaluation results

MiniCPM5-2B capability radar

Benchmark results reported by OpenBMB for the original MiniCPM5-2B checkpoint.

Release

Format Quantization Size
MLX safetensors Affine 4-bit, group size 64 1.4 GB

The model is text-only. No vision projector or MTP files are included.

Usage

pip install -U mlx-lm
mlx_lm.generate \
  --model . \
  --prompt "Explain why reproducible builds matter." \
  --max-tokens 512

Source

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