Instructions to use mlx-community/Inkling-mlx-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Inkling-mlx-2bit 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-2bit")

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-2bit 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-2bit"

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-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Inkling-mlx-2bit 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-2bit"

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-2bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Inkling-mlx-2bit 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-2bit"

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-2bit" \
  --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-2bit 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-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Inkling-mlx-2bit"
# 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-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Inkling-mlx-2bit / README.md

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metadata

license: apache-2.0
library_name: mlx
tags:
  - mlx
  - inkling
  - moe
  - text-generation
base_model: thinkingmachines/Inkling
pipeline_tag: text-generation

Inkling-mlx-2bit (2-bit, text backbone, BF16-sourced)

An MLX 2-bit build of the text backbone of Thinking Machines' Inkling (975B-total / 41B-active MoE), quantized directly from the BF16 checkpoint. The most compact build in the ladder - for multi-Mac distributed experiments.

This is created for community using a one Apple Mac Studio M3 Ultra with 512 GB.

Heads up

Memory: ~329 GB on disk (routed experts at 2-bit, group size 64; attention / shared experts / embeddings / norms kept bf16). Loading needs roughly that much unified memory -> fits a 2x 192 GB Mac Studio distributed setup; does not fit a single Mac.
2-bit quality: experts are quantized hard; this is the lowest-quality rung. For better quality see the 3-bit / 4-bit siblings.
Not verified yet: custom Inkling forward (factorized attention + short-conv + sigmoid MoE) is a from-reference reimplementation; logits not yet checked vs the original.
Scope: text decoder only (no vision/audio).

Ladder

variant	bits	~size	fits
this	2	329 GB	2 Macs
Inkling-mlx-3bit	3	~454 GB	3 Macs
Inkling-mlx	4 (bf16 src)	~560 GB	3-4 Macs
Inkling-NVFP4-mlx	4 (nvfp4 src)	~581 GB	3-4 Macs

Usage (once a loader is available)

from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Inkling-mlx-2bit")
print(generate(model, tokenizer, prompt="The capital of France is", max_tokens=64))