Instructions to use CoreWorxLab/caal-qwen3.5-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CoreWorxLab/caal-qwen3.5-2b 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 CoreWorxLab/caal-qwen3.5-2b
# Run inference directly in the terminal:
llama cli -hf CoreWorxLab/caal-qwen3.5-2b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CoreWorxLab/caal-qwen3.5-2b
# Run inference directly in the terminal:
llama cli -hf CoreWorxLab/caal-qwen3.5-2b

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 CoreWorxLab/caal-qwen3.5-2b
# Run inference directly in the terminal:
./llama-cli -hf CoreWorxLab/caal-qwen3.5-2b

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 CoreWorxLab/caal-qwen3.5-2b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CoreWorxLab/caal-qwen3.5-2b

Use Docker

docker model run hf.co/CoreWorxLab/caal-qwen3.5-2b

LM Studio
Jan
Ollama
How to use CoreWorxLab/caal-qwen3.5-2b with Ollama:
```
ollama run hf.co/CoreWorxLab/caal-qwen3.5-2b
```
Unsloth Desktop

How to use CoreWorxLab/caal-qwen3.5-2b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CoreWorxLab/caal-qwen3.5-2b

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": "CoreWorxLab/caal-qwen3.5-2b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CoreWorxLab/caal-qwen3.5-2b with Docker Model Runner:
```
docker model run hf.co/CoreWorxLab/caal-qwen3.5-2b
```

Lemonade

How to use CoreWorxLab/caal-qwen3.5-2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CoreWorxLab/caal-qwen3.5-2b

Run and chat with the model

lemonade run user.caal-qwen3.5-2b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use CoreWorxLab/caal-qwen3.5-2b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CoreWorxLab/caal-qwen3.5-2b

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 CoreWorxLab/caal-qwen3.5-2b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CoreWorxLab/caal-qwen3.5-2b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CoreWorxLab/caal-qwen3.5-2b

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 "CoreWorxLab/caal-qwen3.5-2b" \
  --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"

caal-qwen3.5-2b

File size: 1,873 Bytes

a5965ce

---
license: other
license_name: caal-model-license-v1
license_link: LICENSE
language:
  - en
base_model: Qwen/Qwen3.5-2B
tags:
  - tool-calling
  - voice-assistant
  - fine-tuned
  - gguf
  - qwen3.5
  - caal
---

# CAAL Qwen3.5 2B — Fine-Tuned for Tool Calling

A fine-tuned [Qwen3.5 2B](https://huggingface.co/Qwen/Qwen3.5-2B) model optimized for tool calling in voice assistant workflows. Built for [CAAL](https://github.com/CoreWorxLab/CAAL) (CoreWorxLab Ambient Assistant for Linux).

## Performance

**82/85 tests passed (96%)** on the CAAL 85-test evaluation suite:

| Category | Score |
|----------|-------|
| Single tool calls | 27/27 |
| Conversational (no tool) | 10/10 |
| Multi-turn chains | 28/29 |
| Argument formatting | 17/19 |

## Model Details

- **Base model**: Qwen3.5 2B
- **Training method**: SFT with BF16 LoRA (last-turn-only — previous turns as context, only final response trained)
- **LoRA config**: r=32, alpha=32
- **Quantization**: Q4_K_M (GGUF)
- **File size**: ~1.2 GB
- **VRAM usage**: ~2.6 GB at 16384 context

## Usage with Ollama

```bash
# Download the GGUF and create a Modelfile:
# Modelfile contents:
# FROM caal-qwen3.5-2b-q4.gguf
# RENDERER qwen3.5
# PARSER qwen3.5
# PARAMETER temperature 0.1
# PARAMETER num_ctx 16384

ollama create caal-qwen35-2b -f Modelfile
```

## Designed For

- Edge deployment on consumer GPUs (fits on 5GB+ VRAM alongside TTS)
- Local voice assistants with tool calling
- Smart home control, email, calendar, and service management
- Multi-step tool chains (e.g., search → lookup contact → send email)

## License

See [LICENSE](LICENSE) for the CAAL Model License v1.0. This model is free for personal, non-commercial use with attribution to CoreWorxLab. Commercial use requires written permission.

The base model (Qwen3.5) is licensed under Apache 2.0. Users must comply with both licenses.