Instructions to use ANTRUhub/ANTRU-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ANTRUhub/ANTRU-1.5B 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 ANTRUhub/ANTRU-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ANTRUhub/ANTRU-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ANTRUhub/ANTRU-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ANTRUhub/ANTRU-1.5B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ANTRUhub/ANTRU-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ANTRUhub/ANTRU-1.5B:Q4_K_M

Use Docker

docker model run hf.co/ANTRUhub/ANTRU-1.5B:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use ANTRUhub/ANTRU-1.5B with Ollama:
```
ollama run hf.co/ANTRUhub/ANTRU-1.5B:Q4_K_M
```
Unsloth Desktop

How to use ANTRUhub/ANTRU-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ANTRUhub/ANTRU-1.5B: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": "ANTRUhub/ANTRU-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ANTRUhub/ANTRU-1.5B with Docker Model Runner:
```
docker model run hf.co/ANTRUhub/ANTRU-1.5B:Q4_K_M
```

Lemonade

How to use ANTRUhub/ANTRU-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ANTRUhub/ANTRU-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.ANTRU-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ANTRUhub/ANTRU-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ANTRUhub/ANTRU-1.5B: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 ANTRUhub/ANTRU-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ANTRUhub/ANTRU-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ANTRUhub/ANTRU-1.5B: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 "ANTRUhub/ANTRU-1.5B: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"

ANTRU-1.5B

A self-trained 1.5B parameter coding assistant, fine-tuned from Qwen2.5-Coder-1.5B-Instruct on a custom curriculum of 302 examples covering coding, mathematics, reasoning, tool use, and identity.

Model Details

Base model: Qwen2.5-Coder-1.5B-Instruct (Apache-2.0)
Fine-tune: LoRA (r=16, alpha=32) on 302 curated examples
Quantization: Q4_K_M (~941MB GGUF)
Context: 128K (YaRN)

Training

Trained locally on consumer hardware (RTX 5000 Ada laptop GPU / i7-8550U CPU) using LoRA (r=16, alpha=32, dropout=0.05) for 1 epoch over 302 examples covering:

Identity & personality ("I am ANTRU...")
Coding tasks (algorithms, debugging, explanations)
Mathematics and reasoning
Tool use (read_file, execute_command, write_file)
Multi-turn dialogue patterns

Usage

License

Apache-2.0 (inherited from Qwen2.5-Coder)

Attribution

Based on Qwen2.5-Coder-1.5B-Instruct by Alibaba Cloud's Qwen team (Apache-2.0).

Downloads last month: 49

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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