Instructions to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("rapid-mlx/Ling-3.0-tiny-MLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rapid-mlx/Ling-3.0-tiny-MLX-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rapid-mlx/Ling-3.0-tiny-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rapid-mlx/Ling-3.0-tiny-MLX-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "rapid-mlx/Ling-3.0-tiny-MLX-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "rapid-mlx/Ling-3.0-tiny-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rapid-mlx/Ling-3.0-tiny-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "rapid-mlx/Ling-3.0-tiny-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use rapid-mlx/Ling-3.0-tiny-MLX-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rapid-mlx/Ling-3.0-tiny-MLX-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rapid-mlx/Ling-3.0-tiny-MLX-4bit

Run Hermes

hermes

Ling-3.0-tiny-MLX-4bit / README.md

Raullen

docs: rapid-mlx 0.12.10 released — serve requirement now a plain version

328a497 verified 1 day ago

preview code

Raw

History Blame Contribute Delete

2.02 kB

	---
	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.