Instructions to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF # Run inference directly in the terminal: llama cli -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF # Run inference directly in the terminal: llama cli -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF # Run inference directly in the terminal: ./llama-cli -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
Use Docker
docker model run hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
- LM Studio
- Jan
- vLLM
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-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": "cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
- Ollama
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with Ollama:
ollama run hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
- Unsloth Studio
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF to start chatting
- Pi
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
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 cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
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 "cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF" \ --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"
- Docker Model Runner
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with Docker Model Runner:
docker model run hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
- Lemonade
How to use cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF
Run and chat with the model
lemonade run user.qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF-{{QUANT_TAG}}List all available models
lemonade list
Fix Modelfile: add RENDERER/PARSER qwen3.5 for tool calling; relative GGUF path; remove hardcoded TEMPLATE override
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PARAMETER temperature 0.6
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PARAMETER top_p 0.95
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# Modelfile for Ollama — qwen3.5-9b-qwen3.6-reasoning-distilled
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#
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# CRITICAL: `RENDERER qwen3.5` and `PARSER qwen3.5` are REQUIRED for tool
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# calling / function calling to work. Without them Ollama falls back to
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# generic text handling, `ollama show` reports "does not support tools",
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# and /api/chat ignores the `tools` field.
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#
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# The embedded chat template in the GGUF already supports tool use. These
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# two directives wire Ollama's native Go renderer + parser for Qwen3.5 so
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# the model's <tool_call> output gets translated to structured
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# message.tool_calls in the API response.
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#
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# Default here uses the Q4_K_M (~5.6 GB) quant. To use Q8_0 (~9.5 GB)
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# replace the FROM line with:
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# FROM ./qwen3.5-9b-qwen3.6-reasoning-distilled.q80.gguf
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# To use F16 (~17.9 GB):
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# FROM ./qwen3.5-9b-qwen3.6-reasoning-distilled.f16.gguf
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#
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# To create the model locally after downloading the GGUF + this Modelfile:
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# ollama create qwen3.5-9b-qwen3.6-distilled:q4km -f Modelfile
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# ollama show qwen3.5-9b-qwen3.6-distilled:q4km # should list: tools, thinking
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# ollama run qwen3.5-9b-qwen3.6-distilled:q4km
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#
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# Or pull directly from this HF repo (specify the quant as the tag):
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# ollama pull hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF:Q4_K_M
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# ollama pull hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF:Q8_0
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# ollama pull hf.co/cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF:F16
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# NOTE: Direct `hf.co/...` pulls do not apply this Modelfile — Ollama will
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# auto-derive one from GGUF metadata. If the auto-derived model loses tool
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# support, download this Modelfile + the GGUF and `ollama create` manually.
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FROM ./qwen3.5-9b-qwen3.6-reasoning-distilled.q4km.gguf
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RENDERER qwen3.5
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PARSER qwen3.5
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PARAMETER temperature 0.6
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PARAMETER top_p 0.95
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PARAMETER stop "<|im_end|>"
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