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

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

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

⚠️ CRITICAL: Ollama Inference Flag Required

If you serve this model via Ollama with the qwen3.5 RENDERER (the standard recommended setup), you MUST pass "think": false in the /api/chat request body for chat / instruction following / tool use.
curl -X POST http://localhost:11434/api/chat \
  -d '{"model": "...", "think": false, "messages": [...], "stream": false}'
Without this flag, the renderer auto-injects <think> tags into every chat completion. On longer prompts the model can stay inside the <think> block past the response budget, never emit </think>, and produce zero answer tokens on 25-46% of requests.

Set think: true (or omit) only when you DO want chain-of-thought reasoning (math, planning, complex multi-step). This is Qwen3 dual-mode operation per https://qwenlm.github.io/blog/qwen3/.

See the dataset cudabenchmarktest/r9-research-framework _OLLAMA_INFERENCE_WARNING.md for the full explanation.

Qwen3.5-9B Reasoning Distilled GGUF (R3 Crown)

GGUF quantized version of fine-tuned Qwen3.5-9B with distilled Opus 4.6 reasoning traces. Early iteration (R3) — superseded by R7 (86.8% diverse eval).

Training

Base model: Qwen/Qwen3.5-9B
Method: LoRA SFT (r=32, alpha=64, LR=2e-4)
Data: Crownelius/Opus-4.6-Reasoning-3300x (2160 samples)
Training suite: robit-man/fine_tuning_suite

Note

This early iteration had regressions in instruction following due to monoculture training data. See the training suite for the improved R5/R7 approach.

Successors

Model	Eval	Link
R7 Research	86.8%	cudabenchmarktest/qwen3.5-9b-r7-research
R7 Vision	86.8%	cudabenchmarktest/qwen3.5-9b-r7-research-vision

License

Apache 2.0 (inherited from Qwen3.5-9B).

Downloads last month: 166

GGUF

Model size

10B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

(514)

this model

cudabenchmarktest
/

qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF

⚠️ CRITICAL: Ollama Inference Flag Required

Qwen3.5-9B Reasoning Distilled GGUF (R3 Crown)

Training

Note

Successors

License

Model tree for cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF

Dataset used to train cudabenchmarktest/qwen3.5-9b-qwen3.6-reasoning-distilled-GGUF