Instructions to use ghostai1/Egypt_Historical7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghostai1/Egypt_Historical7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ghostai1/Egypt_Historical7b",
	filename="history-egypt7b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ghostai1/Egypt_Historical7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/Egypt_Historical7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/Egypt_Historical7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/Egypt_Historical7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/Egypt_Historical7b: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 ghostai1/Egypt_Historical7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ghostai1/Egypt_Historical7b: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 ghostai1/Egypt_Historical7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ghostai1/Egypt_Historical7b:Q4_K_M

Use Docker

docker model run hf.co/ghostai1/Egypt_Historical7b:Q4_K_M

LM Studio
Jan

vLLM

How to use ghostai1/Egypt_Historical7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ghostai1/Egypt_Historical7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ghostai1/Egypt_Historical7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ghostai1/Egypt_Historical7b:Q4_K_M

Ollama
How to use ghostai1/Egypt_Historical7b with Ollama:
```
ollama run hf.co/ghostai1/Egypt_Historical7b:Q4_K_M
```

Unsloth Studio new

How to use ghostai1/Egypt_Historical7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ghostai1/Egypt_Historical7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ghostai1/Egypt_Historical7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghostai1/Egypt_Historical7b to start chatting

Pi new

How to use ghostai1/Egypt_Historical7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/Egypt_Historical7b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ghostai1/Egypt_Historical7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ghostai1/Egypt_Historical7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/Egypt_Historical7b: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 ghostai1/Egypt_Historical7b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ghostai1/Egypt_Historical7b with Docker Model Runner:
```
docker model run hf.co/ghostai1/Egypt_Historical7b:Q4_K_M
```

Lemonade

How to use ghostai1/Egypt_Historical7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ghostai1/Egypt_Historical7b:Q4_K_M

Run and chat with the model

lemonade run user.Egypt_Historical7b-Q4_K_M

List all available models

lemonade list

Historical Egyptology 7B (GGUF)

Historical Egyptology 7B — a Mistral-7B-Instruct fine-tune infused with the grandeur, mystery, and wisdom of ancient Egypt.
Perfect for immersive historical roleplay, myth retellings, educational narratives, pharaonic decrees, scribe chronicles, and Nile-soaked storytelling.

COMPILING DATA WIP check tomm morn should be fully out

✨ Overview

This model was fine-tuned (LoRA r=64, alpha=64, 3 epochs, 860 cleaned examples) on historical Egyptology texts using Unsloth.
It excels at evocative, period-flavored prose — invoking gods (Ra, Anubis, Isis, Thoth), pharaohs, dynasties, mummification rites, pyramid construction, Nile mythology, and the atmosphere of temples and tombs.

Best uses:

Deep historical fiction & alternate-history tales
Educational explainers (mummification, Book of the Dead, Old/Middle/New Kingdom)
Roleplay as pharaoh, high priest, scribe, tomb robber, explorer, deity…
Creative local assistants with ancient-Egypt personality

Not optimized for:

Strict modern factual accuracy (flavor > precision)
Advanced math, coding, or technical reasoning
Extremely long context without rope/extensions

Training snapshot (2026-02-05 run):

Base: Mistral-7B-Instruct (~7.4B params)
LoRA: 167,772,160 trainable params (2.26%)
Dataset: 860 examples, max seq len 3072
Epochs: 3 | Steps: 162 | Final loss: 1.7763
Runtime: ~11.5 hours (1× GPU)

📜 Model Details

Model name: Historical Egyptology 7B
Base model: Mistral-7B-Instruct
Fine-tuning: LoRA (merged via Unsloth)
Parameters: ~7.4 billion
Context length: 3072 native (tested up to 8192+ in llama.cpp)
Language: English + ancient Egyptian stylistic terms
License: MIT (subject to base model license)

🗿 Quantized Files

All files are from the same merged fine-tune checkpoint — only quantization level changes.

File	Quant	Bits	Approx. Size	VRAM est. (4k ctx)	Recommendation
`egypt-7b-v1.TQ1_0.gguf`	TQ1_0	~1	~1.5 GB	< 2 GB	Ultra-low memory (experimental)
`egypt-7b-v1.Q2_K.gguf`	Q2_K	~2.5	~2.6 GB	~3 GB	Very low RAM
`egypt-7b-v1.Q3_K_S.gguf`	Q3_K_S	~3.5	~3.0 GB	~3.5 GB	Low-memory sweet spot
`egypt-7b-v1.Q3_K_M.gguf`	Q3_K_M	~3.8	~3.3 GB	~4 GB	Balanced low-RAM
`egypt-7b-v1.Q4_K_S.gguf`	Q4_K_S	~4.5	~3.8 GB	~4.5 GB	Good quality / low VRAM
`egypt-7b-v1.Q4_K_M.gguf`	Q4_K_M	~4.8	~4.1 GB	~5 GB	Default – best overall balance
`egypt-7b-v1.Q5_K_S.gguf`	Q5_K_S	~5.5	~4.6 GB	~5.5 GB	Higher quality
`egypt-7b-v1.Q5_K_M.gguf`	Q5_K_M	~5.7	~4.8 GB	~6 GB	Recommended for best quality
`egypt-7b-v1.Q6_K.gguf`	Q6_K	~6.6	~5.4 GB	~6.5 GB	Very good detail
`egypt-7b-v1.Q8_0.gguf`	Q8_0	8	~7.1 GB	~8 GB	Near-lossless reference

Quick picks:

Most users → Q4_K_M or Q5_K_M (great balance on 6–8 GB cards)
Tight hardware → Q3_K_M or Q4_K_S
Maximum fidelity → Q8_0

🚀 Usage Examples (llama.cpp)

CLI – basic generation

./llama-cli \
  -m egypt-7b-v1.Q4_K_M.gguf \
  -ngl 35 \                        # adjust GPU layers (0 = CPU only)
  -c 4096 \
  --color \
  -p "You are a high priest of Amun-Ra in Karnak during the reign of Thutmose III. A young acolyte asks you to explain the sacred meaning of the benben stone and its connection to creation. Speak in solemn, evocative language." \
  -n 1024

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit