Instructions to use Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit 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("Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit")

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 Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit"

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": "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit 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 "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit"

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 Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit

Run Hermes

hermes

MLX LM

How to use Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniCPM5-1B MLX DWQ 4-bit

MLX 4-bit quantization of openbmb/MiniCPM5-1B.

Quantization

Runtime: MLX / mlx-lm
Bits: 4
Group size: 64
Mode: affine
Observed packed size: about 580 MB
Conversion log: mlx_lm.convert reported 4.501 bits per weight

Smoke Test

Local Apple M2 smoke test:

Prompt tokens: 17
Prompt speed: 28.621 tok/s
Generation speed: 133.897 tok/s
Peak memory: 0.674 GB

Validation Status

Runtime loading and generation work, but this artifact is not yet release-approved on the strict multilingual/code/tool-use validation matrix. Known failures include Persian response quality, Arabic arithmetic response quality, and reasoning leakage on tool-planning prompts.

Usage

pip install -U mlx-lm
mlx_lm.generate --model Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit --prompt "Hello" --max-tokens 64

Downloads last month: 222

Safetensors

Model size

0.2B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for Reza2kn/MiniCPM5-1B-MLX-DWQ-4bit

Base model

openbmb/MiniCPM5-1B

Quantized

(42)

this model