Instructions to use Pries/Priestess_q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pries/Priestess_q8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pries/Priestess_q8",
	filename="Priestess_q8.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 Pries/Priestess_q8 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 Pries/Priestess_q8
# Run inference directly in the terminal:
llama cli -hf Pries/Priestess_q8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Pries/Priestess_q8
# Run inference directly in the terminal:
llama cli -hf Pries/Priestess_q8

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 Pries/Priestess_q8
# Run inference directly in the terminal:
./llama-cli -hf Pries/Priestess_q8

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 Pries/Priestess_q8
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pries/Priestess_q8

Use Docker

docker model run hf.co/Pries/Priestess_q8

LM Studio
Jan
Ollama
How to use Pries/Priestess_q8 with Ollama:
```
ollama run hf.co/Pries/Priestess_q8
```

Unsloth Studio

How to use Pries/Priestess_q8 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 Pries/Priestess_q8 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 Pries/Priestess_q8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pries/Priestess_q8 to start chatting

How to use Pries/Priestess_q8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pries/Priestess_q8

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": "Pries/Priestess_q8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Pries/Priestess_q8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pries/Priestess_q8

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 Pries/Priestess_q8

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Pries/Priestess_q8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pries/Priestess_q8

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 "Pries/Priestess_q8" \
  --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"

Docker Model Runner
How to use Pries/Priestess_q8 with Docker Model Runner:
```
docker model run hf.co/Pries/Priestess_q8
```

Lemonade

How to use Pries/Priestess_q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pries/Priestess_q8

Run and chat with the model

lemonade run user.Priestess_q8-{{QUANT_TAG}}

List all available models

lemonade list

Priestess_q8 / README.md

Pries

Update README.md

29af32a verified about 2 months ago

preview code

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	---
	license: other
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- llama-factory
	- lora
	- gguf
	- roleplay
	- arknights
	language:
	- en
	- zh
	---

	# Priestess - AI Persona Project

	This project features a personalized chatbot based on the Qwen2.5-7B-Instruct model. It was fine-tuned using the LlamaFactory framework and LoRA (Low-Rank Adaptation) technology to simulate the character Priestess from the game Arknights.

	## Project Background

	Traditional prompting often struggles to maintain complex character settings and worldview knowledge over the long term. This project aims to utilize the LlamaFactory framework combined with LoRA technology to inject exclusive knowledge from the game Arknights at a low cost. The goal is to create an intelligent chatbot that deeply understands the game's worldview and simulates specific character speech patterns.

	## Technical Specifications

	* Training Framework: LlamaFactory.
	* Fine-tuning Method: LoRA (Low-Rank Adaptation).
	* Software Requirements: Python 3.10, PyTorch 2.6.0, and Transformers 4.50.0.
	* Hardware Compatibility: Optimized for CUDA-enabled GPUs or Apple Silicon via GGUF/MLX.

	## Dataset Construction

	The quality of the training set significantly affects the model's performance; therefore, quality was prioritized over quantity.

	1. Data Extraction: Character dialogue lines were extracted from Arknights wiki source code using regular expressions.
	2. Manual Curation: High-quality baseline dialogues were manually written first to establish character consistency.
	3. Layered Expansion: AI was guided to perform layered expansion based on game text and worldview background.
	4. Format: The dataset follows the Alpaca registration format.

	## Deployment and Usage (GGUF & Ollama)

	The project includes instructions for converting safetensors to GGUF format and performing quantization for efficient local inference.

	### Running with Ollama

	1. Prepare a Modelfile:
	```dockerfile
	FROM ./qwen_q8.gguf
	PARAMETER temperature 0.8
	PARAMETER top_p 0.9
	PARAMETER repeat_penalty 1.1
	SYSTEM "You are Priestess from Arknights..."