Instructions to use dauksza123/Chris_Dauksza with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dauksza123/Chris_Dauksza with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dauksza123/Chris_Dauksza",
	filename="model-170ce5accbab.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 dauksza123/Chris_Dauksza with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dauksza123/Chris_Dauksza
# Run inference directly in the terminal:
llama-cli -hf dauksza123/Chris_Dauksza

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dauksza123/Chris_Dauksza
# Run inference directly in the terminal:
llama-cli -hf dauksza123/Chris_Dauksza

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 dauksza123/Chris_Dauksza
# Run inference directly in the terminal:
./llama-cli -hf dauksza123/Chris_Dauksza

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 dauksza123/Chris_Dauksza
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dauksza123/Chris_Dauksza

Use Docker

docker model run hf.co/dauksza123/Chris_Dauksza

LM Studio
Jan

vLLM

How to use dauksza123/Chris_Dauksza with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dauksza123/Chris_Dauksza"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dauksza123/Chris_Dauksza",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dauksza123/Chris_Dauksza

Ollama
How to use dauksza123/Chris_Dauksza with Ollama:
```
ollama run hf.co/dauksza123/Chris_Dauksza
```

Unsloth Studio new

How to use dauksza123/Chris_Dauksza 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 dauksza123/Chris_Dauksza 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 dauksza123/Chris_Dauksza to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dauksza123/Chris_Dauksza to start chatting

Pi new

How to use dauksza123/Chris_Dauksza with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dauksza123/Chris_Dauksza

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": "dauksza123/Chris_Dauksza"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dauksza123/Chris_Dauksza with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dauksza123/Chris_Dauksza

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 dauksza123/Chris_Dauksza

Run Hermes

hermes

Docker Model Runner
How to use dauksza123/Chris_Dauksza with Docker Model Runner:
```
docker model run hf.co/dauksza123/Chris_Dauksza
```

Lemonade

How to use dauksza123/Chris_Dauksza with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dauksza123/Chris_Dauksza

Run and chat with the model

lemonade run user.Chris_Dauksza-{{QUANT_TAG}}

List all available models

lemonade list

Chris Dauksza AI Model

This repository contains a custom AI model tailored for generating complete, functional, and optimized code, designed with 32,768-token context and robust configuration options. The model reflects the expertise and communication style of Chris Dauksza, the founder of StarMaint AI, StarReliability AI, and EZMaint.

Overview

The Chris Dauksza AI model is based on the "huihui_ai/qwq-fusion" model and is optimized for:

Generating functional and complete code.
Handling large contexts for multi-step problem-solving.
Precision in responses tailored to industrial AI and automation applications.

Features

32,768-Token Context: Supports extensive context windows for handling complex tasks and long prompts.
Code Optimization: Fine-tuned to produce clean, efficient, and logically structured code.
Dynamic Configuration: Includes options for controlling temperature, sampling, and repetition penalties.
Multimodal Support: Accepts text and image inputs for advanced use cases.
Tool Integration: Enables calling external tools and functions for extended capabilities.

Model Specifications

Base Model: huihui_ai/qwq-fusion
Token Limit: 32,768
Temperature: 0.2 (optimized for deterministic outputs)
Sampling Methods:
- top_k: 20
- top_p: 0.6
- repeat_penalty: 1.2
Custom System Prompt: Encodes the expertise and communication style of Chris Dauksza.

Usage

Prerequisites

Install Ollama CLI for managing and running models. TO RUN ollama run chris_dauksza_ai Example Request Use the Ollama API to send a generation request:

Request: { "model": "chris_dauksza_ai", "prompt": "Write a Python function to calculate factorial using recursion.", "options": { "temperature": 0.2, "top_k": 20, "top_p": 0.6 } } Response: { "response": "def factorial(n):\n if n == 0 or n == 1:\n return 1\n return n * factorial(n - 1)" } Advanced Configuration The model supports a variety of parameters to customize its behavior:

Parameter Description Default Value num_ctx Context window size 32768 temperature Creativity level 0.2 top_k Limits sampling to the top-k tokens 20 top_p Cumulative probability threshold 0.6 repeat_penalty Penalty for repeating tokens 1.2 mirostat Dynamic perplexity control 2 Development This repository also includes API code that integrates with the model for generating, chatting, and embedding functionality. See the api directory for details.

Contributions Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions for improvement.

License This project is licensed under the MIT License. See the LICENSE file for details.

Contact For questions or support, contact Chris Dauksza: - Email: chrisdauksza@gmail.com - LinkedIn: Chris Dauksza - Website: dauksza.fun

Empowering technology, one line of code at a time. “`