Instructions to use hotepfederales/hotep-kush-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotepfederales/hotep-kush-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotepfederales/hotep-kush-v2",
	filename="gguf/Meta-Llama-3.1-8B-Instruct.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hotepfederales/hotep-kush-v2 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-v2:Q8_0
# Run inference directly in the terminal:
llama-cli -hf hotepfederales/hotep-kush-v2: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-v2:Q8_0
# Run inference directly in the terminal:
llama-cli -hf hotepfederales/hotep-kush-v2: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-v2:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf hotepfederales/hotep-kush-v2: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-v2:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotepfederales/hotep-kush-v2:Q8_0

Use Docker

docker model run hf.co/hotepfederales/hotep-kush-v2:Q8_0

LM Studio
Jan
Ollama
How to use hotepfederales/hotep-kush-v2 with Ollama:
```
ollama run hf.co/hotepfederales/hotep-kush-v2:Q8_0
```

Unsloth Studio

How to use hotepfederales/hotep-kush-v2 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-v2 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-v2 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-v2 to start chatting

How to use hotepfederales/hotep-kush-v2 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-v2: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-v2:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotepfederales/hotep-kush-v2 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-v2: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-v2:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use hotepfederales/hotep-kush-v2 with Docker Model Runner:
```
docker model run hf.co/hotepfederales/hotep-kush-v2:Q8_0
```

Lemonade

How to use hotepfederales/hotep-kush-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotepfederales/hotep-kush-v2:Q8_0

Run and chat with the model

lemonade run user.hotep-kush-v2-Q8_0

List all available models

lemonade list

Hotep Intelligence Kush V2 — Fallback Model

ℹ️ Fallback: Kush V2 is the production fallback model. Current production is Kush V3 (100/100 eval, CoT reasoning, +46% verbosity).

Hotep Intelligence LLM — Kush V2, fine-tuned on Llama 3.1 8B Instruct. The first Llama-based model in the Hotep line — eliminated Qwen2.5 CJK leakage and rubric injection entirely.

Model Details

Parameter	Value
Base model	meta-llama/Llama-3.1-8B-Instruct
Training	785 SFT + 658 IPO pairs
Quality eval	85/100, 0% CJK, 0% rubric leakage
Status	Fallback (superseded by Kush V3)

Files

File	Type	Notes
`model-*.safetensors`	Merged model	Full precision weights
`gguf/Meta-Llama-3.1-8B-Instruct.Q8_0.gguf`	GGUF Q8_0	For Ollama (~8.1 GB)

Usage with Ollama

ollama run hotepfederales/hotep-kush-v2

Current Production Model

ollama run hotepfederales/hotep-llm-kush-v3

hotepfederales/hotep-kush-v3 — 1,594 SFT + 791 IPO, CoT reasoning, 100/100 quality.

Live at: https://askhotep.ai | https://t.me/hotep_llm_bot

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

BF16

Inference Providers NEW

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Model tree for hotepfederales/hotep-kush-v2

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Adapter

(2394)

this model