Instructions to use SBDO1/SBD04 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/SBD04 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/SBD04:F16
# Run inference directly in the terminal:
llama cli -hf SBDO1/SBD04:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SBDO1/SBD04:F16
# Run inference directly in the terminal:
llama cli -hf SBDO1/SBD04: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 SBDO1/SBD04:F16
# Run inference directly in the terminal:
./llama-cli -hf SBDO1/SBD04: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 SBDO1/SBD04:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SBDO1/SBD04:F16

Use Docker

docker model run hf.co/SBDO1/SBD04:F16

LM Studio
Jan
Ollama
How to use SBDO1/SBD04 with Ollama:
```
ollama run hf.co/SBDO1/SBD04:F16
```
Unsloth Desktop

How to use SBDO1/SBD04 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SBDO1/SBD04:F16

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/SBD04:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SBDO1/SBD04 with Docker Model Runner:
```
docker model run hf.co/SBDO1/SBD04:F16
```

Lemonade

How to use SBDO1/SBD04 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SBDO1/SBD04:F16

Run and chat with the model

lemonade run user.SBD04-F16

List all available models

lemonade list

Hermes Agent

How to use SBDO1/SBD04 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/SBD04: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 SBDO1/SBD04:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SBDO1/SBD04 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SBDO1/SBD04: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 "SBDO1/SBD04: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"

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Check out the documentation for more information.

SBD04 - Custom Coding AI Assistant

A fine-tuned language model optimized for programming tasks, built on Qwen2.5-Coder-1.5B-Instruct architecture.

📦 Models

File	Size	Description
`sbd04-qwen2.5-coder-1.5b-q4_k_m.gguf`	~1.1 GB	Recommended - Quantized Q4_K_M, fast inference
`sbd04-qwen2.5-coder-1.5b-f16.gguf`	~3 GB	Full precision F16

🚀 Quick Start

LM Studio

Download sbd04-qwen2.5-coder-1.5b-q4_k_m.gguf
Open LM Studio → My Models → Add Model → From File
Select the GGUF file
Set context length to 4096+ and start coding

llama.cpp

./main -m sbd04-qwen2.5-coder-1.5b-q4_k_m.gguf -p "Write a Python function to parse JSON" -n 512

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="sbd04-qwen2.5-coder-1.5b-q4_k_m.gguf", n_ctx=4096)
output = llm("Write a binary search in Python", max_tokens=512)
print(output['choices'][0]['text'])

🎓 Training Details

Base Model: Qwen2.5-Coder-1.5B-Instruct
Method: LoRA fine-tuning (r=16, α=32)
Dataset: 60 coding Q&A pairs covering Python, JavaScript, TypeScript, SQL, Git, Docker, REST APIs, Design Patterns, Debugging, Performance, Security, and more
Epochs: 3
Trainable params: ~18.5M (1.18% of model)
Max context: 1024 tokens

💡 Example Prompts

"Write a Python decorator for timing functions"
"How do I implement a binary search tree in Python?"
"Review this code for bugs: [paste code]"
"Explain async/await in JavaScript"
"How to prevent SQL injection in Python?"
"Write a Dockerfile for a FastAPI app"
"What's the difference between INNER JOIN and LEFT JOIN?"
"How to use TypeScript generics?"
"Optimize this slow Python loop: [paste code]"
"Write a pytest fixture for database testing"

📄 License

Apache-2.0

🔗 Links

Hugging Face: SBDO1/SBD04
Created by: SBDO

Built with ❤️ for developers everywhere

Downloads last month: 49

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

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SBDO1
/

SBD04

SBD04 - Custom Coding AI Assistant

📦 Models

🚀 Quick Start

LM Studio

llama.cpp

Python (llama-cpp-python)

🎓 Training Details

💡 Example Prompts

📄 License

🔗 Links

Space using SBDO1/SBD04 1