Instructions to use wcamon/Agents-A1-4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wcamon/Agents-A1-4B-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("wcamon/Agents-A1-4B-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 wcamon/Agents-A1-4B-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 "wcamon/Agents-A1-4B-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": "wcamon/Agents-A1-4B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use wcamon/Agents-A1-4B-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 "wcamon/Agents-A1-4B-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 wcamon/Agents-A1-4B-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use wcamon/Agents-A1-4B-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 "wcamon/Agents-A1-4B-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 "wcamon/Agents-A1-4B-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 wcamon/Agents-A1-4B-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 "wcamon/Agents-A1-4B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "wcamon/Agents-A1-4B-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": "wcamon/Agents-A1-4B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Agents-A1-4B · MLX 4-bit

Unofficial MLX conversion for Tippi.

This repository is an unofficial text-only MLX conversion of InternScience/Agents-A1-4B. It is not an official InternScience release. The source checkpoint and this conversion are distributed under Apache-2.0; retain the upstream notices and attribution when redistributing.

Conversion

Upstream revision: 945c40a4aa6f534d434a353207b8d42ecf7a5293
Converter: MLX-LM 0.31.3
Quantization: affine 4-bit
Group size: 64
Average bits per weight: 4.503

hf download InternScience/Agents-A1-4B \
  --revision 945c40a4aa6f534d434a353207b8d42ecf7a5293 \
  --local-dir Agents-A1-4B-source

mlx_lm.convert \
  --hf-path Agents-A1-4B-source \
  --mlx-path Agents-A1-4B-4bit \
  --quantize \
  --q-bits 4 \
  --q-group-size 64

The artifact contains the Qwen 3.5 text model only: 924 tensors under language_model, with no vision tower or projector weights.

Tippi usage

Tippi loads this model locally through MLX Swift LM 3.31.3. It applies the checkpoint chat template with enable_thinking=false, uses temperature 0, and does not provide tools, web search, filesystem access, or cloud inference.

Local reference measurement

On the development M4 Pro Mac, a Traditional Chinese non-thinking probe generated 160 tokens at 85.7 tokens/s, prefilling 58 tokens at 350 tokens/s, with 2.63 GB peak MLX memory and 3.28 seconds process time. These are machine-specific research observations, not release guarantees.

Attribution

Source model: https://huggingface.co/InternScience/Agents-A1-4B
MLX-LM: https://github.com/ml-explore/mlx-lm
Tippi integration: https://github.com/wcAmon/catcher-tippi

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Safetensors

Model size

0.7B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

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