Instructions to use SBDO1/SBD03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SBDO1/SBD03 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 SBDO1/SBD03:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SBDO1/SBD03:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SBDO1/SBD03:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SBDO1/SBD03:Q4_K_M

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 SBDO1/SBD03:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SBDO1/SBD03:Q4_K_M

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 SBDO1/SBD03:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SBDO1/SBD03:Q4_K_M

Use Docker

docker model run hf.co/SBDO1/SBD03:Q4_K_M

LM Studio
Jan
Ollama
How to use SBDO1/SBD03 with Ollama:
```
ollama run hf.co/SBDO1/SBD03:Q4_K_M
```
Unsloth Desktop

How to use SBDO1/SBD03 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SBDO1/SBD03: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": "SBDO1/SBD03:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SBDO1/SBD03 with Docker Model Runner:
```
docker model run hf.co/SBDO1/SBD03:Q4_K_M
```

Lemonade

How to use SBDO1/SBD03 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SBDO1/SBD03:Q4_K_M

Run and chat with the model

lemonade run user.SBD03-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use SBDO1/SBD03 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SBDO1/SBD03: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 SBDO1/SBD03:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SBDO1/SBD03 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SBDO1/SBD03: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 "SBDO1/SBD03: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"

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

SBD03 - Custom Educational AI Tutor

A fine-tuned language model optimized for educational Q&A, built on Qwen2.5-1.5B-Instruct architecture.

📦 Models

File	Size	Description
`SBD03-q4_k_m.gguf`	986 MB	Recommended - Quantized Q4_K_M, fast inference
`SBD03-f16.gguf`	2.95 GB	Full precision F16
`SBD03-base-q4_k_m.gguf`	1.1 GB	Original Qwen2.5-1.5B-Instruct baseline

🚀 Quick Start (LM Studio, llama.cpp, Python)

🎓 Training Details

Base: Qwen2.5-1.5B-Instruct
LoRA: r=16, α=32, 3 epochs
72 educational Q&A pairs
Loss: 2.03 → 0.61

💡 Example Prompts

"How do I solve 2x + 5 = 15?"
"Explain the water cycle simply"
"What's the difference between a list and dictionary in Python?"

📄 License: Apache-2.0

Developed by: [SBD]
Model type: [qwen2]
License: [Apache-2.0]
Finetuned from model [qwen 2.5]:

We don not know what to do

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

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Model tree for SBDO1/SBD03

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

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