Instructions to use hotepfederales/hotep-kush-v4-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use hotepfederales/hotep-kush-v4-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hotepfederales/hotep-kush-v4-gguf", filename="Meta-Llama-3.1-8B-Instruct.Q8_0-001.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hotepfederales/hotep-kush-v4-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hotepfederales/hotep-kush-v4-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hotepfederales/hotep-kush-v4-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
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 hotepfederales/hotep-kush-v4-gguf:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
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 hotepfederales/hotep-kush-v4-gguf:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
Use Docker
docker model run hf.co/hotepfederales/hotep-kush-v4-gguf:Q8_0
- LM Studio
- Jan
- vLLM
How to use hotepfederales/hotep-kush-v4-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hotepfederales/hotep-kush-v4-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hotepfederales/hotep-kush-v4-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hotepfederales/hotep-kush-v4-gguf:Q8_0
- Ollama
How to use hotepfederales/hotep-kush-v4-gguf with Ollama:
ollama run hf.co/hotepfederales/hotep-kush-v4-gguf:Q8_0
- Unsloth Studio
How to use hotepfederales/hotep-kush-v4-gguf 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 hotepfederales/hotep-kush-v4-gguf 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 hotepfederales/hotep-kush-v4-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hotepfederales/hotep-kush-v4-gguf to start chatting
- Pi
How to use hotepfederales/hotep-kush-v4-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
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": "hotepfederales/hotep-kush-v4-gguf:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use hotepfederales/hotep-kush-v4-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf hotepfederales/hotep-kush-v4-gguf:Q8_0
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 hotepfederales/hotep-kush-v4-gguf:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use hotepfederales/hotep-kush-v4-gguf with Docker Model Runner:
docker model run hf.co/hotepfederales/hotep-kush-v4-gguf:Q8_0
- Lemonade
How to use hotepfederales/hotep-kush-v4-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hotepfederales/hotep-kush-v4-gguf:Q8_0
Run and chat with the model
lemonade run user.hotep-kush-v4-gguf-Q8_0
List all available models
lemonade list
Hotep Intelligence Kush V4 โ GGUF
GGUF quantizations of hotepfederales/hotep-kush-v4, the current production model powering askhotep.ai and @hotep_llm_bot.
Available Quantizations
| File | Quantization | Size | Use Case |
|---|---|---|---|
hotep-kush-v4.Q4_K_M.gguf |
Q4_K_M | ~4.7 GB | Recommended โ production default |
hotep-kush-v4.Q8_0.gguf |
Q8_0 | ~8.5 GB | Max quality, needs 12+ GB RAM |
Model Details
| Parameter | Value |
|---|---|
| Base model | meta-llama/Meta-Llama-3.1-8B-Instruct |
| LoRA rank | 32, alpha 32, RSLoRA |
| max_seq_length | 4096 |
| Quality eval | 100/100, 0% CJK, 0% rubric leakage |
| Persona consistency | 10/10 |
Key Features
- Deep reasoning traces โ structured chain-of-thought before every response
- Expanded knowledge base โ African history, economics, and philosophy
- Stronger Ma'at alignment โ cleaner, culturally grounded answers
- Context retention โ handles complex multi-turn conversations
Quick Start (Ollama)
ollama run hotep-llm-kush-v4
Quick Start (llama-cpp-python)
from llama_cpp import Llama
llm = Llama(model_path="hotep-kush-v4.Q4_K_M.gguf", n_ctx=4096)
output = llm("What is the principle of Ma'at?", max_tokens=512)
print(output["choices"][0]["text"])
About Hotep Intelligence
Sovereign AI trained on African history, philosophy, and wisdom traditions โ no corporate surveillance, no data collection.
- Website: askhotep.ai
- Telegram bot: @hotep_llm_bot
- Knowledge base: knowledge.askhotep.ai
- Base model (safetensors): hotepfederales/hotep-kush-v4
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Model tree for hotepfederales/hotep-kush-v4-gguf
Base model
meta-llama/Llama-3.1-8B