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num_loops=2) with shared weights, giving 4B total parameters
with the capacity of a deeper model.nanbeige model type is pending upstream support in mlx-lm
(ml-explore/mlx-lm#1597).
Until that merges, install from the PR branch:pip install git+https://github.com/MercuriusDream/mlx-lm.git@add-nanbeige-modelmlx_lm.generate --model MercuriusDream/Nanbeige4.2-3B-mlx-5bit --prompt "hello"1from mlx_lm import load, generate
2
3model, tokenizer = load("MercuriusDream/Nanbeige4.2-3B-mlx-5bit")
4prompt = tokenizer.apply_chat_template(
5 [{"role": "user", "content": "hello"}], add_generation_prompt=True
6)
7print(generate(model, tokenizer, prompt=prompt))