Instructions to use mlx-community/Inkling-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Inkling-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("mlx-community/Inkling-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 mlx-community/Inkling-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 "mlx-community/Inkling-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": "mlx-community/Inkling-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Inkling-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 "mlx-community/Inkling-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 mlx-community/Inkling-mlx-4bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Inkling-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 "mlx-community/Inkling-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 "mlx-community/Inkling-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 mlx-community/Inkling-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 "mlx-community/Inkling-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Inkling-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": "mlx-community/Inkling-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Inkling-mlx (4-bit, text backbone, BF16-sourced)

An MLX 4-bit build of the text backbone of Thinking Machines' Inkling (975B-total / 41B-active MoE), quantized directly from the BF16 checkpoint (no NVFP4->INT4 double-quantization), for running natively on Apple Silicon with mlx-lm.

Community note. Early, not fully numerically-verified conversion, shared to see whether anyone can load/run a model this large on Apple Silicon. Expect rough edges; please open a discussion with results (or failures).

Heads up

Memory: ~560 GB on disk (4-bit routed experts + bf16 attention / shared experts / embeddings). Loading needs roughly that much unified memory - beyond any single Mac today (max 512 GB), so realistically distributed/multi-device MLX. Largely a research artifact.
Not verified yet: the custom Inkling forward (factorized attention + short-conv + sigmoid MoE) is a from-reference reimplementation; logits have not been checked against the original.
Scope: text decoder only. Vision (image/video) and audio encoders are not included.

Provenance

Source: thinkingmachines/Inkling (BF16) -> MLX affine 4-bit (group size 64). Only routed MoE experts are quantized; everything else is bf16. Quantizing straight from BF16 avoids the NVFP4->INT4 step, so this should be slightly higher quality than the NVFP4-sourced sibling (huckiyang/Inkling-NVFP4-mlx).

Usage (once a loader is available)

from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Inkling-mlx-4bit")
print(generate(model, tokenizer, prompt="The capital of France is", max_tokens=64))

The custom model class lives in the conversion repo (models/inkling_mlx.py); until it's registered in mlx-lm, load via that module's load().

Blog: https://huckiyang.github.io/blog/inkling-audio-design.html

Downloads last month: -

Safetensors

Model size

164B params

Tensor type

BF16

U32

MLX

Hardware compatibility

Quantized

Model tree for mlx-community/Inkling-mlx-4bit

Base model

thinkingmachines/Inkling

Finetuned

(3)

this model

Collection including mlx-community/Inkling-mlx-4bit

Inkling MLX

Collection

MLX versions of Inkling 975B-A41B, omni to text • 2 items • Updated about 4 hours ago • 1