Instructions to use Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aa09876/SmallThinker-4BA0.6B-Instruct-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("Aa09876/SmallThinker-4BA0.6B-Instruct-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 Aa09876/SmallThinker-4BA0.6B-Instruct-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 "Aa09876/SmallThinker-4BA0.6B-Instruct-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": "Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Aa09876/SmallThinker-4BA0.6B-Instruct-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 "Aa09876/SmallThinker-4BA0.6B-Instruct-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 Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit

Run Hermes

hermes

OpenClaw new

How to use Aa09876/SmallThinker-4BA0.6B-Instruct-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 "Aa09876/SmallThinker-4BA0.6B-Instruct-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 "Aa09876/SmallThinker-4BA0.6B-Instruct-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 Aa09876/SmallThinker-4BA0.6B-Instruct-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 "Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Aa09876/SmallThinker-4BA0.6B-Instruct-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": "Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

SmallThinker-4BA0.6B-Instruct (MLX 4-bit)

An MLX 4-bit (group size 64, affine) conversion of PowerInfer/SmallThinker-4BA0.6B-Instruct: a 4B-total / roughly 0.6B-active dense-attention Mixture-of-Experts model (32 experts, top-4, ReLU-gated), non-thinking Instruct.

Requires an mlx_lm architecture module

Stock mlx_lm does not yet ship a smallthinker architecture, so this model will not load with an unmodified install. Until the upstream mlx-lm PR lands, copy the included smallthinker.py into your mlx_lm/models/ directory:

cp smallthinker.py "$(python -c 'import mlx_lm,os;print(os.path.join(os.path.dirname(mlx_lm.__file__),"models"))')/"

Then:

python -m mlx_lm generate \
  --model Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit \
  --prompt "Explain a mixture-of-experts model in two sentences."

Tool calling

The model uses the native Hermes tool-call format, <tool_call>{json}</tool_call>, and works with servers that select a Hermes tool parser (for example rapid-mlx --tool-call-parser hermes, no reasoning parser).

Provenance and lineage

Base weights: PowerInfer/SmallThinker-4BA0.6B-Instruct (Apache-2.0), pinned revision b51db6d
Architecture support: a from-scratch mlx_lm implementation of the custom SmallThinkerForCausalLM (ReLU-gated packed-expert MoE, GQA with 12 query / 2 KV heads, and the model's pre-attention router-input routing). It was numerically parity-checked against the reference PyTorch model: BF16 greedy generation matched token-for-token, router top-k selection matched 100% (tie-adjusted), and per-tensor error stayed at the BF16 rounding floor.
Quantization: MLX 4-bit, group size 64, affine.

License

Apache-2.0, inherited from the base model. Attribution: PowerInfer / IPADS (SmallThinker). This repository redistributes a quantized derivative under the same license.

Downloads last month: 10

Safetensors

Model size

0.6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for Aa09876/SmallThinker-4BA0.6B-Instruct-mlx-4bit

Base model

Tiiny/SmallThinker-4BA0.6B-Instruct

Quantized

(10)

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