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abenzerps/ZDTaichu5.0-9B-GGUF
ZDTaichu5.0-9B-GGUF 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 gguf. The card lists the license as apache-2.0.
[](https://abenzerps.com/request-a-model/)
Downloads · 30 days
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.gguf48.9 GB · 100%
From the Hugging Face model README
GGUF 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 model supports a context length of up to 128K tokens (131,072).
For accelerated text generation via speculative decoding, this model can be paired with the companion draft model abenzerps/ZDTaichu5.0-9B-DSpark-GGUF.

Benchmark results reported by TaichuAI for the original ZDTaichu5.0-9B foundation model. These figures are not measurements of this GGUF conversion.
| Quantization | File | Size (GB) |
|---|---|---|
| Q8_0 | ZDTaichu5.0-9B-Q8_0.gguf | 9.53 GB |
| Q6_K | ZDTaichu5.0-9B-Q6_K.gguf | 7.36 GB |
| Q5_K_M | ZDTaichu5.0-9B-Q5_K_M.gguf | 6.47 GB |
| Q4_K_M | ZDTaichu5.0-9B-Q4_K_M.gguf | 5.63 GB |
| Q4_0 | ZDTaichu5.0-9B-Q4_0.gguf | 5.31 GB |
| Q3_K_M | ZDTaichu5.0-9B-Q3_K_M.gguf | 4.62 GB |
| IQ3_M | ZDTaichu5.0-9B-IQ3_M.gguf | 4.42 GB |
| IQ3_XXS | ZDTaichu5.0-9B-IQ3_XXS.gguf | 3.94 GB |
IQ3_M and IQ3_XXS use importance-matrix quantization.
| File | Size | Description |
|---|---|---|
| mmproj-ZDTaichu5.0-9B-BF16.gguf | 1.65 GB | BF16 vision projector for runtimes with multimodal support |
The projector is optional for text-only use. Use it with a current llama.cpp build that supports the model's multimodal path.
The GGUF embeds the upstream chat template. An external copy is provided as chat_template.jinja for runtimes that require a separate template file.
For text generation with llama.cpp:
llama-cli \
-m ZDTaichu5.0-9B-Q4_K_M.gguf \
-c 8192 --jinja \
--temp 0.7 --top-p 0.95 \
-p "Explain why reproducible builds matter."
For multimodal vision understanding with llama.cpp:
llama-cli \
-m ZDTaichu5.0-9B-Q4_K_M.gguf \
--mmproj mmproj-ZDTaichu5.0-9B-BF16.gguf \
--image path/to/image.jpg \
-p "Describe what is shown in this image in detail."
For an OpenAI-compatible server:
llama-server \
-m ZDTaichu5.0-9B-Q4_K_M.gguf \
--mmproj mmproj-ZDTaichu5.0-9B-BF16.gguf \
-c 8192 --jinja --host 0.0.0.0 --port 8080
Increase -c up to 131072 when sufficient memory is available. Tool-call behavior depends on the serving runtime and its parser integration; use the embedded template and verify tool calls in the target application.
Accelerate inference using the companion ZDTaichu5.0-9B-DSpark-GGUF draft model:
llama-cli \
-m ZDTaichu5.0-9B-Q4_K_M.gguf \
-md ZDTaichu5.0-9B-DSpark-Q4_0.gguf \
-c 8192 --jinja \
-p "Explain why reproducible builds matter."
Or with llama-server:
llama-server \
-m ZDTaichu5.0-9B-Q4_K_M.gguf \
-md ZDTaichu5.0-9B-DSpark-Q4_0.gguf \
-c 8192 --jinja --host 0.0.0.0 --port 8080