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-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("mlx-community/Inkling-mlx-2bit")
config = load_config("mlx-community/Inkling-mlx-2bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

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 @earendil-works/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

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

Atomic Chat

OpenClaw

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"

huckiyang commited on Jul 24

Commit

6467b43

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1 Parent(s): 4c1d7e2

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@@ -42,7 +42,7 @@ print(tok.decode(greedy_generate(model, config, ids, max_new_tokens=64)))
 ## Run on a single Mac with SSD expert-offload
-~315 GB doesn't fit a 192 GB Mac resident — but an MoE only fires **6 of 256** experts per
 token, so you can keep the always-needed weights in RAM (attention, shared experts, embeddings,
 norms, router, vision/audio towers ≈ 35 GB) and **page the routed experts from SSD on demand**,
 letting the OS page cache hold the hot ones. This makes the 2-bit omni build runnable on one

 ## Run on a single Mac with SSD expert-offload
+~315 GB doesn't fit a 192 GB Mac resident but an MoE only fires **6 of 256** experts per
 token, so you can keep the always-needed weights in RAM (attention, shared experts, embeddings,
 norms, router, vision/audio towers ≈ 35 GB) and **page the routed experts from SSD on demand**,
 letting the OS page cache hold the hot ones. This makes the 2-bit omni build runnable on one