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

Libraries

How to use ToPo-ToPo/Inkling-Small-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("ToPo-ToPo/Inkling-Small-mlx-2bit")
config = load_config("ToPo-ToPo/Inkling-Small-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 ToPo-ToPo/Inkling-Small-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 "ToPo-ToPo/Inkling-Small-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": "ToPo-ToPo/Inkling-Small-mlx-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ToPo-ToPo/Inkling-Small-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 "ToPo-ToPo/Inkling-Small-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 "ToPo-ToPo/Inkling-Small-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"

Hermes Agent

How to use ToPo-ToPo/Inkling-Small-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 "ToPo-ToPo/Inkling-Small-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 ToPo-ToPo/Inkling-Small-mlx-2bit

Run Hermes

hermes

Atomic Chat

ToPo-ToPo/Inkling-Small-mlx-2bit

MLX 2bit conversion of thinkingmachines/Inkling-Small for Apple Silicon (mlx-vlm). 276B total / 12B active sparse MoE (42 layers, 256 routed experts top-6 + 2 shared), text + image + audio in, text out.

Requires mlx-vlm >= 0.6.9

0.6.9 is the first release whose models/inkling can load an official Inkling checkpoint through the public loader, and the first that implements the MoE global_scale / gate.bias tensors. On 0.6.7 / 0.6.8 this repo will not load.

from mlx_vlm import load, generate
model, processor = load("ToPo-ToPo/Inkling-Small-mlx-2bit")

The config is the official schema, unmodified — no key translation and no loader patches are needed.

Provenance (self-converted from official weights)

Source: thinkingmachines/Inkling-Small (license: apache-2.0, bf16, 531.9 GB)
Tool: mlx-vlm 0.6.9 — mlx_vlm.convert --hf-path thinkingmachines/Inkling-Small --mlx-path . -q --q-bits 2 --q-group-size 64
Effective: 2.506 bits/weight (77 GiB on disk)
Only edit on top of the conversion: pad_token / eos_token added to tokenizer_config.json (the official TokenizersBackend config sets neither, so transformers raises on any padded call). Both point at existing ids — the vocabulary is unchanged.

Reasoning effort

The chat template always injects a Thinking effort level: system message (default 0.9). Control it with the OpenAI-compatible reasoning_effort — "none" / "minimal" / "low" / "medium" / "high" / "max", or a float in [0.0, 0.99]. "none" disables thinking entirely.

Revision history

2026-08-04 — reconverted with mlx-vlm 0.6.9. The previous upload had been converted with 0.6.7, whose models/inkling did not implement the MoE mlp.global_scale (50 keys) and mlp.gate.bias (40 keys) present in the official checkpoint, so those tensors were silently dropped. It also shipped a translated config (renamed intermediate_size / dense_intermediate_size, etc.) that 0.6.9 rejects. If you pulled this repo before this date, re-download it.

Downloads last month: 225

Safetensors

Model size

25B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

2-bit

Model tree for ToPo-ToPo/Inkling-Small-mlx-2bit

Base model

thinkingmachines/Inkling-Small

Quantized

(42)

this model