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

Libraries

How to use PyThaGo/LLMLit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PyThaGo/LLMLit",
	filename="LLMLit-0.2-8B-Instruct.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PyThaGo/LLMLit with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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 PyThaGo/LLMLit
# Run inference directly in the terminal:
./llama-cli -hf PyThaGo/LLMLit

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

Use Docker

docker model run hf.co/PyThaGo/LLMLit

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

Unsloth Studio new

How to use PyThaGo/LLMLit 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 PyThaGo/LLMLit 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 PyThaGo/LLMLit to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use PyThaGo/LLMLit with Pi:

Start the llama.cpp server

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

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": "PyThaGo/LLMLit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use PyThaGo/LLMLit with Hermes Agent:

Start the llama.cpp server

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

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 PyThaGo/LLMLit

Run Hermes

hermes

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

Lemonade

How to use PyThaGo/LLMLit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PyThaGo/LLMLit

Run and chat with the model

lemonade run user.LLMLit-{{QUANT_TAG}}

List all available models

lemonade list

Cristian Sas commited on Jan 30, 2025

Commit

1954067

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1 Parent(s): 590de60

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@@ -332,7 +332,7 @@ Metavers AI Assistant with LLMLit" 🌐🤖
 ---
 IoT AI Assistant with LLMLit 🌍🧠
 Descriere:
-"IoT AI Assistant with LLMLit" este o aplicație inteligentă care combină LLMLit și Internet of Things (IoT) pentru a oferi un asistent virtual avansat. Acesta poate înțelege întrebări complexe, oferi recomandări personalizate și controla dispozitive IoT în timp real. Cu suport pentru interacțiune vocală și text, asistentul optimizează eficiența și automatizarea în medii smart home, industriale sau business.
 ---

 ---
 IoT AI Assistant with LLMLit 🌍🧠
 Descriere:
+"IoT AI Assistant with LLMLit" va fi o aplicație inteligentă care combină LLMLit și Internet of Things (IoT) pentru a oferi un asistent virtual avansat. Acesta poate înțelege întrebări complexe, oferi recomandări personalizate și controla dispozitive IoT în timp real. Cu suport pentru interacțiune vocală și text, asistentul optimizează eficiența și automatizarea în medii smart home, industriale sau business.
 ---