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ZDTaichu5.0-9B-MLX-4bit
abenzerps/ZDTaichu5.0-9B-MLX-4bit
ZDTaichu5.0-9B-MLX-4bit is a image-text-to-text model from abenzerps. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
[](https://abenzerps.com/request-a-model/)
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.safetensors6.7 GB · 100%
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How the weights are stored.
U329B · 91%
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Model card
From the Hugging Face model README
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
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- zdtaichu5_0
- License
- apache-2.0
- Languages
- en, zh
- Created
- Sep 16, 2026
- Updated
- Sep 16, 2026