Instructions to use inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF" --prompt "Once upon a time"
MiMo-V2.5-Pro
No longer available on HF due to storage restrictions - archived here
See MiMo-V2.5-Pro in action: demonstration videos
Tested with an M3 Ultra 512 GiB and M4 Max 128 GiB RAM using Inferencer app distributed compute
- Distributed inference: ~13 tokens/s @ 1000 tokens ~450 GiB / ~67 GiB (debug build)
Model tree for inferencerlabs/MiMo-V2.5-Pro-MLX-Q4.3-INF
Base model
XiaomiMiMo/MiMo-V2.5-Pro