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

c288941

verified ·

1 Parent(s): d4bd123

Update README.md

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@@ -47,6 +47,20 @@ metrics:
 | 📂 **Resurse**  | [GitHub Repository](#) / Paper: *To be published* |
 | 🚀 **Demo**  | *Coming Soon* |
 ---
 ### **Coming Soon: Modele de Generare Imagine și Video 🎨🎬**
 ---

 | 📂 **Resurse**  | [GitHub Repository](#) / Paper: *To be published* |
 | 🚀 **Demo**  | *Coming Soon* |
+### **Sistem Recomandat pentru LLMLit – Performanță Echilibrată**
+| Componentă        | Model Recomandat                 | Specificații Cheie                         | Emojis      |
+|------------------|-----------------------------------|--------------------------------------------|-------------|
+| **Procesor (CPU)** | AMD Ryzen 7 7800X3D / Intel i7-13700K | 8C/16T, 5.0 GHz boost, cache mare    | ⚡🖥️       |
+| **Placă Video (GPU)** | NVIDIA RTX 4070 / AMD RX 7800 XT    | 12GB GDDR6, AI cores, DLSS 3        | 🎮🚀       |
+| **Memorie RAM**   | 32GB DDR5 5600MHz (Corsair / Kingston) | Dual-Channel, CL30, XMP 3.0          | 💾🔥       |
+| **Stocare (SSD)** | 1TB NVMe Gen4 (Samsung 980 Pro)  | 7000 MB/s Read, 5000 MB/s Write            | 💽⚡       |
+| **Placă de bază** | MSI B650 Tomahawk Wi-Fi         | PCIe 4.0, Wi-Fi 6E, USB-C                   | 🔩📡       |
+| **Sistem de operare** | Windows 11 Pro / Ubuntu 22.04 | Optimizat pentru AI și productivitate     | 🖥️🛠️       |
+🔹 **Acest sistem este ideal pentru rularea LLMLit fără probleme, oferind un echilibru perfect între performanță și eficiență.**
 ---
 ### **Coming Soon: Modele de Generare Imagine și Video 🎨🎬**
 ---