Instructions to use abenzerps/ZDTaichu5.0-9B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abenzerps/ZDTaichu5.0-9B-MLX-4bit 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("abenzerps/ZDTaichu5.0-9B-MLX-4bit")
config = load_config("abenzerps/ZDTaichu5.0-9B-MLX-4bit")

# 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 abenzerps/ZDTaichu5.0-9B-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 "abenzerps/ZDTaichu5.0-9B-MLX-4bit"

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": "abenzerps/ZDTaichu5.0-9B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use abenzerps/ZDTaichu5.0-9B-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 "abenzerps/ZDTaichu5.0-9B-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 abenzerps/ZDTaichu5.0-9B-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/ZDTaichu5.0-9B-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 "abenzerps/ZDTaichu5.0-9B-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 "abenzerps/ZDTaichu5.0-9B-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"

ZDTaichu5.0-9B MLX

MLX files for ZDTaichu5.0-9B, an advanced multimodal foundation model developed by the Zi Dong Tai Chu team for visual understanding, spatial reasoning, agentic tool use, and embodied AI workloads. Built on a Qwen3.5-9B language backbone and C-RADIOv4-H vision encoder, the source checkpoint supports a native context length of 131,072 tokens (128K).

Benchmarks

Benchmark results reported by TaichuAI for the original ZDTaichu5.0-9B foundation model. These figures are not measurements of this MLX conversion.

MLX files

Quantization	Repository	Size (GB)
8-bit	ZDTaichu5.0-9B-MLX-8bit	11.20 GB
6-bit	ZDTaichu5.0-9B-MLX-6bit	8.96 GB
4-bit	ZDTaichu5.0-9B-MLX-4bit	6.72 GB

Multimodal architecture

Component	Architecture	Precision
Language Backbone	Qwen3.5 hybrid (Gated DeltaNet + full attention, 3:1 ratio)	Quantized (4-bit affine, group_size=64)
Vision Encoder	C-RADIOv4-H (ViT-H/16, 653M)	BF16 (full precision)
Projector	RMSNorm → Linear(5120→20480) → SquaredReLU → Linear(20480→4096)	BF16 (full precision)

The vision encoder and multimodal projector are kept at full precision (BF16) to prevent visual reasoning degradation.

Chat template

The MLX conversion embeds the upstream chat template. An external copy is provided as chat_template.jinja for runtimes that require a separate template file.

Usage

Vision understanding with `mlx-vlm`

mlx_vlm.generate \
  --model abenzerps/ZDTaichu5.0-9B-MLX-4bit \
  --image path/to/image.jpg \
  --prompt "Describe what is shown in this image in detail." \
  --max-tokens 512 \
  --temp 0.7

Text generation with `mlx-vlm`

mlx_vlm.generate \
  --model abenzerps/ZDTaichu5.0-9B-MLX-4bit \
  --prompt "Explain why reproducible builds matter." \
  --max-tokens 512 \
  --temp 0.7

Python API

from mlx_vlm import load, generate

model_id = "abenzerps/ZDTaichu5.0-9B-MLX-4bit"
model, processor = load(model_id)

prompt = "Describe what is shown in this image in detail."
image = "path/to/image.jpg"

output = generate(model, processor, prompt=prompt, image=image, max_tokens=512)
print(output)

Increase context up to 131,072 tokens (128K) when sufficient unified memory is available on Apple Silicon. Tool-call behavior depends on the serving runtime and its parser integration; use the embedded template and verify tool calls in the target application.

Source and build

Source model: TaichuAI/ZDTaichu5.0-9B
Source revision: a22afd15a3f85659f103caa659ec4aa9500a998e
Conversion: ml-explore/mlx / ml-explore/mlx-vlm
Quantization format: MLX affine quantization (group_size=64, bits=4)
License: Apache-2.0

Downloads last month: 49

Safetensors

Model size

10B params

Tensor type

U32

BF16

I64

MLX

Hardware compatibility

4-bit

Model tree for abenzerps/ZDTaichu5.0-9B-MLX-4bit

Base model

TaichuAI/ZDTaichu5.0-9B

Quantized

(6)

this model

Collection including abenzerps/ZDTaichu5.0-9B-MLX-4bit

ZDTaichu5.0-9B

Collection

ZDTaichu5.0-9B model family releases, including GGUF and MLX conversions. • 5 items • Updated about 22 hours ago

ZDTaichu5.0-9B MLX

Benchmarks

MLX files

Multimodal architecture

Chat template

Usage

Vision understanding with mlx-vlm

Text generation with mlx-vlm

Python API

Source and build

Model tree for abenzerps/ZDTaichu5.0-9B-MLX-4bit

Collection including abenzerps/ZDTaichu5.0-9B-MLX-4bit

Vision understanding with `mlx-vlm`

Text generation with `mlx-vlm`