Instructions to use abenzerps/ZDTaichu5.0-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use abenzerps/ZDTaichu5.0-9B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use abenzerps/ZDTaichu5.0-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abenzerps/ZDTaichu5.0-9B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abenzerps/ZDTaichu5.0-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
- Ollama
How to use abenzerps/ZDTaichu5.0-9B-GGUF with Ollama:
ollama run hf.co/abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use abenzerps/ZDTaichu5.0-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use abenzerps/ZDTaichu5.0-9B-GGUF with Docker Model Runner:
docker model run hf.co/abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
- Lemonade
How to use abenzerps/ZDTaichu5.0-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ZDTaichu5.0-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use abenzerps/ZDTaichu5.0-9B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
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-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use abenzerps/ZDTaichu5.0-9B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf abenzerps/ZDTaichu5.0-9B-GGUF:Q4_K_M
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-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --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 GGUF
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.
Benchmarks
Benchmark results reported by TaichuAI for the original ZDTaichu5.0-9B foundation model. These figures are not measurements of this GGUF conversion.
GGUF files
| 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.
Multimodal projector
| 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.
Chat template
The GGUF embeds the upstream chat template. An external copy is provided as chat_template.jinja for runtimes that require a separate template file.
Usage
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.
Speculative Decoding (with DSpark)
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
Source and build
- Source model: TaichuAI/ZDTaichu5.0-9B
- Source revision: a22afd15a3f85659f103caa659ec4aa9500a998e
- Conversion: ggml-org/llama.cpp commit 7ceed8737fdb4eb09b4760e77bd12d38012de5a8
- License: Apache-2.0
- Checksums: SHA256SUMS.txt
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Model tree for abenzerps/ZDTaichu5.0-9B-GGUF
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