Instructions to use wang-yang/Agents-A1-MTPLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wang-yang/Agents-A1-MTPLX-Q4 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("wang-yang/Agents-A1-MTPLX-Q4")

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 wang-yang/Agents-A1-MTPLX-Q4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"

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": "wang-yang/Agents-A1-MTPLX-Q4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use wang-yang/Agents-A1-MTPLX-Q4 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 "wang-yang/Agents-A1-MTPLX-Q4"

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 wang-yang/Agents-A1-MTPLX-Q4

Run Hermes

hermes

OpenClaw new

How to use wang-yang/Agents-A1-MTPLX-Q4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"

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 "wang-yang/Agents-A1-MTPLX-Q4" \
  --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 wang-yang/Agents-A1-MTPLX-Q4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "wang-yang/Agents-A1-MTPLX-Q4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "wang-yang/Agents-A1-MTPLX-Q4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Agents-A1-MTPLX-Q4

4-bit quantized MLX version of InternScience/Agents-A1 with grafted MTP (Multi-Token Prediction) head for speculative decoding on Apple Silicon.

Model Details

Base model: InternScience/Agents-A1 (Qwen3.5-MoE architecture, 35B total / 3B active parameters)
Quantization: 4-bit affine (group size 64), router gates at 8-bit
MTP head: Grafted from Qwen3.5-35B-A3B (4-bit quantized, 1 layer)
Format: MLX safetensors
Disk size: ~18 GB (model) + 1.6 GB (MTP sidecar)

Architecture

Hidden size: 2048
Layers: 40 (hybrid linear + full attention)
Experts: 256 total, 8 active per token
Vocab: 248,320
Context: 262,144 tokens

Usage with MTPLX

mtplx start --model wang-yang/Agents-A1-MTPLX-Q4

Usage with mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("wang-yang/Agents-A1-MTPLX-Q4")
prompt = "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"
result = generate(model, tokenizer, prompt=prompt, max_tokens=200)

Notes

EOS token: <|im_end|> (id 248046)
MTP speculative decoding: ~1.33x speedup (D2 best, 101.8 tok/s vs AR 76.6 tok/s on M3 Max 128GB).

Files

File	Description
`model-0000X-of-00004.safetensors`	Quantized model weights (4 shards)
`mtp.safetensors`	MTP draft head weights (4-bit quantized)
`config.json`	Model architecture + quantization config
`tokenizer.json`	Tokenizer vocabulary
`tokenizer_config.json`	Tokenizer settings
`chat_template.jinja`	Chat template (no thinking mode)

Downloads last month: 2,230

Safetensors

Model size

35B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for wang-yang/Agents-A1-MTPLX-Q4

Base model

InternScience/Agents-A1

Quantized

(64)

this model