Instructions to use willhar/Qwen3.5-9B-AgentTuned-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 willhar/Qwen3.5-9B-AgentTuned-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 willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

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 willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

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 willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

Use Docker

docker model run hf.co/willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

LM Studio
Jan
Ollama
How to use willhar/Qwen3.5-9B-AgentTuned-GGUF with Ollama:
```
ollama run hf.co/willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
```

Unsloth Studio

How to use willhar/Qwen3.5-9B-AgentTuned-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 willhar/Qwen3.5-9B-AgentTuned-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 willhar/Qwen3.5-9B-AgentTuned-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for willhar/Qwen3.5-9B-AgentTuned-GGUF to start chatting

How to use willhar/Qwen3.5-9B-AgentTuned-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

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": "willhar/Qwen3.5-9B-AgentTuned-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use willhar/Qwen3.5-9B-AgentTuned-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 willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

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 willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use willhar/Qwen3.5-9B-AgentTuned-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

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 "willhar/Qwen3.5-9B-AgentTuned-GGUF:F16" \
  --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 willhar/Qwen3.5-9B-AgentTuned-GGUF with Docker Model Runner:
```
docker model run hf.co/willhar/Qwen3.5-9B-AgentTuned-GGUF:F16
```

Lemonade

How to use willhar/Qwen3.5-9B-AgentTuned-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull willhar/Qwen3.5-9B-AgentTuned-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-9B-AgentTuned-GGUF-F16

List all available models

lemonade list

Model Card for willhar/Qwen3.5-9B-AgentTuned-GGUF

This model is meant to improve upon base Qwen 3.5 9B's tool calling, instruction following, and logical thinking. It uses LORA weights merged into Qwen3.5 9B. 3200 training examples were seen during training, from the four datasets linked in the datasets tab.

Model Evaluation

Benchmarked using https://benchlocal.com/

Temperature = 0.5, default settings otherwise.

Initial Performance of Qwen3.5-9B

BugFind-15 : 84
InstructFollow-15 : 85
ToolCall-15 : 80

Qwen3.5-9B-AgentTuned-GGUF

BugFind-15 : 89
InstructFollow-15 : 90
ToolCall-15 : 93

Downloads last month: 120

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for willhar/Qwen3.5-9B-AgentTuned-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(416)

this model

willhar
/

Qwen3.5-9B-AgentTuned-GGUF

Model Card for willhar/Qwen3.5-9B-AgentTuned-GGUF

Model Evaluation

Model tree for willhar/Qwen3.5-9B-AgentTuned-GGUF

Datasets used to train willhar/Qwen3.5-9B-AgentTuned-GGUF