license: apache-2.0
language:
- en
- zh
library_name: mlx
pipeline_tag: text-generation
tags:
- llm
- nanbeige
- mlx
base_model: Nanbeige/Nanbeige4.2-3B
harshit2312/Nanbeige4.2-3B-mlx-8bit
This is an 8-bit MLX conversion of Nanbeige/Nanbeige4.2-3B, for running on Apple Silicon. Converted from the official weights with mlx-lm 0.31.3 (8-bit, group size 64).
⚠️ Requires a custom architecture module
Nanbeige4.2 is a looped / recurrent-depth transformer (num_loops: 2 — the decoder
stack is executed twice, each pass with its own KV cache). Stock mlx-lm does not
ship a nanbeige architecture, so loading this repo directly will fail with
Model type nanbeige not supported. Install the bundled nanbeige.py into your mlx-lm first:
import os, shutil, mlx_lm
from huggingface_hub import hf_hub_download
src = hf_hub_download("harshit2312/Nanbeige4.2-3B-mlx-8bit", "nanbeige.py")
dst = os.path.join(os.path.dirname(mlx_lm.__file__), "models", "nanbeige.py")
shutil.copy(src, dst)
print("installed nanbeige architecture ->", dst)
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("harshit2312/Nanbeige4.2-3B-mlx-8bit")
messages = [{"role": "user", "content": "What is unified memory on Apple Silicon?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, verbose=True, max_tokens=512)
Note: Nanbeige4.2-3B is a reasoning model — its chat template opens the assistant turn with a
<think>block, so responses begin with visible chain-of-thought.
Verification
The MLX architecture port was validated against the official HuggingFace implementation (fp32, eager attention): 40/40 greedy tokens matched, with the MLX token being the reference's argmax at every position.
Conversion details
| Precision | 8-bit affine, group size 64 (~8.5 bits/weight) |
| Size | ~4.2 GB |
| Architecture | Llama-style (GQA 48/8 heads, head_dim 128, SwiGLU, RoPE θ=70M) + num_loops=2 |