Instructions to use Youssef-dev/science-pharaoh-mini-69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssef-dev/science-pharaoh-mini-69 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Youssef-dev/science-pharaoh-mini-69")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Youssef-dev/science-pharaoh-mini-69", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Youssef-dev/science-pharaoh-mini-69 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 Youssef-dev/science-pharaoh-mini-69:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Youssef-dev/science-pharaoh-mini-69:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Youssef-dev/science-pharaoh-mini-69:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Youssef-dev/science-pharaoh-mini-69: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 Youssef-dev/science-pharaoh-mini-69:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Youssef-dev/science-pharaoh-mini-69:Q4_K_M

Use Docker

docker model run hf.co/Youssef-dev/science-pharaoh-mini-69:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Youssef-dev/science-pharaoh-mini-69 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Youssef-dev/science-pharaoh-mini-69"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Youssef-dev/science-pharaoh-mini-69",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Youssef-dev/science-pharaoh-mini-69:Q4_K_M

SGLang

How to use Youssef-dev/science-pharaoh-mini-69 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Youssef-dev/science-pharaoh-mini-69" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Youssef-dev/science-pharaoh-mini-69",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Youssef-dev/science-pharaoh-mini-69" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Youssef-dev/science-pharaoh-mini-69",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Youssef-dev/science-pharaoh-mini-69 with Ollama:
```
ollama run hf.co/Youssef-dev/science-pharaoh-mini-69:Q4_K_M
```
Unsloth Desktop

How to use Youssef-dev/science-pharaoh-mini-69 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Youssef-dev/science-pharaoh-mini-69: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": "Youssef-dev/science-pharaoh-mini-69:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Youssef-dev/science-pharaoh-mini-69 with Docker Model Runner:
```
docker model run hf.co/Youssef-dev/science-pharaoh-mini-69:Q4_K_M
```

Lemonade

How to use Youssef-dev/science-pharaoh-mini-69 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Youssef-dev/science-pharaoh-mini-69:Q4_K_M

Run and chat with the model

lemonade run user.science-pharaoh-mini-69-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Youssef-dev/science-pharaoh-mini-69 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Youssef-dev/science-pharaoh-mini-69: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 Youssef-dev/science-pharaoh-mini-69:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssef-dev/science-pharaoh-mini-69 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Youssef-dev/science-pharaoh-mini-69: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 "Youssef-dev/science-pharaoh-mini-69: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"

science-pharaoh-mini-69

File size: 1,713 Bytes

---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
tags:
- science
- domain-adaptation
- fine-tuned
- evaluation:gpqa
pipeline_tag: text-generation
language:
- en
datasets:
- custom-science-corpus
model-index:
- name: Science Pharaoh Mini
  results:
  - task:
      type: text-generation
      name: Multiple Choice QA
    dataset:
      name: GPQA Diamond
      type: gpqa_diamond
    metrics:
    - name: Accuracy (Single-Token MC)
      type: accuracy
      value: 34.8
---

# Science Pharaoh Mini

**Science Pharaoh Mini** is a specialized 0.5-parameter language model fine-tuned on scientific reasoning and domain-specific knowledge. Built upon Alibaba Cloud's [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), this model demonstrates optimized performance on graduate-level science reasoning tasks relative to its lightweight footprint.

---

## Key Highlights & Performance

- **Model Size:** 0.5B parameters (Compact, edge-ready)
- **Target Domain:** General Science, Physics, Chemistry, Biology
- **Benchmark Performance:** Reached **34.8%** accuracy on the highly challenging GPQA Diamond benchmark using a single-token evaluation harness.

### Benchmark Evaluation

| Benchmark | Setting | Score | Total Questions | Accuracy |
| :--- | :--- | :--- | :--- | :--- |
| **GPQA Diamond** | Single-Token MC (0-Shot) | 69 / 198 | 198 | **34.8%** |

---

## Quickstart & Usage

### Setup & Directory Layout
The repository artifact package includes all necessary files organized as follows:
```text
.
└── final_model/
    ├── config.json
    ├── model.safetensors
    ├── tokenizer.json
    └── tokenizer_config.json