---
license: mit
base_model: inclusionAI/Ling-3.0-tiny
library_name: mlx
tags:
- mlx
- ling
- bailing_hybrid
- moe
- apple-silicon
pipeline_tag: text-generation
---

# Ling-3.0-tiny — MLX 4-bit

The first MLX conversion of [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny):
a 7.9B-total / **1.3B-active** sparse-MoE reasoner (128 experts, top-8 + 1 shared)
with a KDA + MLA hybrid attention stack and 131K context, MIT licensed.

**4.2 GB at 4.507 bits/weight** — it fits and runs on an 8 GB Apple Silicon Mac.

| | |
|---|---|
| Quantization | 4-bit, group size 64 (router kept 8-bit, short-conv weights fp) |
| Size on disk | 4.2 GB |
| Context | 131,072 tokens |
| Active parameters | 1.3B per token |
| License | MIT (inherited from the base model) |

## Serve it

The `bailing_hybrid` architecture is not in upstream `mlx-lm` yet — this
checkpoint is served by [rapid-mlx](https://rapidmlx.com), which ships a
verified native implementation (reference parity 1.5e-6 against the
official modeling code):

```bash
pip install -U rapid-mlx   # 0.12.10 or newer
rapid-mlx serve ling-3.0-tiny-4bit
```

You get an OpenAI-compatible server on `localhost:8000` with reasoning
(`reasoning_content`) and tool calling parsed natively — thinking is
controlled with `chat_template_kwargs: {"enable_thinking": true}` or the
model's `detailed thinking on/off` system-prompt switch.

Once `mlx-lm` gains native `bailing_hybrid` support, this checkpoint will
load there unchanged.

## Conversion provenance

Converted with `mlx_lm.convert` (quantize=True, q_bits=4, q_group_size=64)
running rapid-mlx's vendored `bailing_hybrid` implementation
([PR #1817](https://github.com/raullenchai/Rapid-MLX/pull/1817)), which was
verified against the official `modeling_bailing_moe_v3.py` on identical
random weights to a max logits deviation of 1.5e-6 (full prefill) /
1.9e-6 (token-by-token incremental) before conversion. End-to-end
chat / reasoning / tool-call behaviour validated on an M2 Pro Mac mini.