MiniCPM-V-4.7-35B-A3B-OptiQ-4bit

mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit#

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. All OptiQ quants · Docs

MiniCPM-V 4.7 35B-A3B, quantized with OptiQ 4-bit, for Apple Silicon.

This is OpenBMB's MiniCPM-V 4.7 35B-A3B, an image-text model with a SigLIP NaViT vision tower and a Qwen3.5-MoE language tower (40 layers, 256 routed experts, 8 active per token). OptiQ keeps 417 of its 510 layers at 8-bit and puts 93 at 4-bit, keeping the vision tower at bf16 in a sidecar file. Its Capability Score of 87.61 is the highest of any MoE we ship and second only to Qwen3.8-27B in the OptiQ lineup. It runs resident on a 36 GB Mac; on smaller machines, --stream-experts on keeps about 4.6 GB resident at the cost of speed.

It works on image and text#

Both ran through this quantized model with optiq serve, MLX only:

Image (the words "OPTIQ 42" on a white background, "What text is in the image?"):

OPTIQ 42

Text ("What is the capital of France? One word."):

Paris

What it is#

Want it smaller? MiniCPM-V-4.7-35B-A3B-OptiQ-4bit-REAP-19B removes half the routed experts: 13.5 GB, Capability Score 81.91.

Capability Score#

Six-metric mean (the standard OptiQ text eval).

Run it#

MiniCPM-V 4.7 needs OptiQ 0.5.25 or later. Stock mlx-lm does not know the minicpmv4_7 architecture, so import optiq registers it, and OptiQ loads the vision sidecar.

pip install "mlx-optiq>=0.5.25"

For image input, serve it with an OpenAI- and Anthropic-compatible endpoint:

optiq serve --model mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit

For the measured mixed-precision KV cache, fetch kv_config.json from this repo and pass it in:

hf download mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit kv_config.json --local-dir .
optiq serve --model mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit --kv-config kv_config.json

Then send an image as image_url content. Text-only generation also loads directly:

import optiq  # registers minicpmv4_7 and the vision sidecar
from mlx_lm import load, generate

model, tok = load("mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit")
msgs = [{"role": "user", "content": "Explain why the sky is blue."}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
print(generate(model, tok, prompt=prompt, max_tokens=512))

On a Mac with less than 36 GB, stream the experts from the SSD:

optiq serve --model mlx-community/MiniCPM-V-4.7-35B-A3B-OptiQ-4bit --stream-experts on

Links#

• Project website: mlx-optiq.com
• Family guide: mlx-optiq.com/docs/minicpm
• All OptiQ quants: mlx-optiq.com/models
• Base model: openbmb/MiniCPM-V-4.7-35B-A3B