Instructions to use PyThaGo/LLMLit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 Settings
- llama.cpp
How to use PyThaGo/LLMLit 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 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 serve -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
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
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 serve -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 serve -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
- Atomic Chat new
- OpenClaw new
How to use PyThaGo/LLMLit with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PyThaGo/LLMLit
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 "PyThaGo/LLMLit" \ --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 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -98,6 +98,92 @@ outputs = model.generate(**inputs, max_length=100)
|
|
| 98 |
|
| 99 |
---
|
| 100 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 101 |
🔗 **Mai multe detalii:** [LLMLit on Hugging Face](https://huggingface.co/LLMLit) 🚀
|
| 102 |
|
| 103 |
|
|
|
|
| 98 |
|
| 99 |
---
|
| 100 |
|
| 101 |
+
Sigur! Iată o documentație simplă și clară despre cum să instalezi **Ollama** și să rulezi **LLMLit** de pe Hugging Face.
|
| 102 |
+
|
| 103 |
+
---
|
| 104 |
+
|
| 105 |
+
# **📌 Ghid de Instalare: Ollama + LLMLit**
|
| 106 |
+
|
| 107 |
+
## **🔹 Pasul 1: Instalarea Ollama**
|
| 108 |
+
Ollama este un framework ușor pentru rularea modelelor **LLM (Large Language Models)** local.
|
| 109 |
+
|
| 110 |
+
### **🖥️ Pentru macOS & Linux**
|
| 111 |
+
1️⃣ **Deschide un terminal și rulează:**
|
| 112 |
+
```sh
|
| 113 |
+
curl -fsSL https://ollama.com/install.sh | sh
|
| 114 |
+
```
|
| 115 |
+
2️⃣ **Repornește terminalul pentru a aplica modificările.**
|
| 116 |
+
|
| 117 |
+
### **🖥️ Pentru Windows (Necesită WSL2)**
|
| 118 |
+
1️⃣ **Activează WSL2 și instalează Ubuntu:**
|
| 119 |
+
- Deschide **PowerShell** ca administrator și rulează:
|
| 120 |
+
```powershell
|
| 121 |
+
wsl --install
|
| 122 |
+
```
|
| 123 |
+
- Repornește computerul.
|
| 124 |
+
|
| 125 |
+
2️⃣ **Instalează Ollama în WSL2:**
|
| 126 |
+
```sh
|
| 127 |
+
curl -fsSL https://ollama.com/install.sh | sh
|
| 128 |
+
```
|
| 129 |
+
|
| 130 |
+
3️⃣ **Verifică dacă Ollama este instalat corect:**
|
| 131 |
+
```sh
|
| 132 |
+
ollama
|
| 133 |
+
```
|
| 134 |
+
Dacă apare meniul de utilizare, instalarea a fost realizată cu succes! 🎉
|
| 135 |
+
|
| 136 |
+
---
|
| 137 |
+
|
| 138 |
+
## **🔹 Pasul 2: Instalarea LLMLit de pe Hugging Face**
|
| 139 |
+
LLMLit poate fi descărcat și rulat în Ollama folosind comanda `ollama pull`.
|
| 140 |
+
|
| 141 |
+
1️⃣ **Deschide un terminal și rulează:**
|
| 142 |
+
```sh
|
| 143 |
+
ollama pull llmlit/LLMLit-0.2-8B-Instruct
|
| 144 |
+
```
|
| 145 |
+
|
| 146 |
+
2️⃣ **Verifică dacă modelul a fost instalat:**
|
| 147 |
+
```sh
|
| 148 |
+
ollama list
|
| 149 |
+
```
|
| 150 |
+
Ar trebui să vezi **LLMLit** în lista de modele disponibile. ✅
|
| 151 |
+
|
| 152 |
+
---
|
| 153 |
+
|
| 154 |
+
## **🔹 Pasul 3: Rularea LLMLit în Ollama**
|
| 155 |
+
După instalare, poți începe să interacționezi cu **LLMLit** astfel:
|
| 156 |
+
|
| 157 |
+
```sh
|
| 158 |
+
ollama run llmlit/LLMLit-0.2-8B-Instruct
|
| 159 |
+
```
|
| 160 |
+
Aceasta va deschide o sesiune locală unde poți discuta cu modelul. 🤖
|
| 161 |
+
|
| 162 |
+
Pentru a trimite un prompt personalizat:
|
| 163 |
+
```sh
|
| 164 |
+
ollama run llmlit/LLMLit-0.2-8B-Instruct "Salut, cum pot folosi LLMLit?"
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
---
|
| 168 |
+
|
| 169 |
+
## **🔹 Pasul 4: Utilizarea LLMLit în Python**
|
| 170 |
+
Dacă vrei să integrezi **LLMLit** într-un script Python, instalează librăria necesară:
|
| 171 |
+
```sh
|
| 172 |
+
pip install ollama
|
| 173 |
+
```
|
| 174 |
+
|
| 175 |
+
Apoi, creează un script Python:
|
| 176 |
+
```python
|
| 177 |
+
import ollama
|
| 178 |
+
|
| 179 |
+
response = ollama.chat(model='llmlit/LLMLit-0.2-8B-Instruct', messages=[{'role': 'user', 'content': 'Cum funcționează LLMLit?'}])
|
| 180 |
+
print(response['message']['content'])
|
| 181 |
+
```
|
| 182 |
+
|
| 183 |
+
---
|
| 184 |
+
|
| 185 |
+
🚀 **Gata!** Acum ai **Ollama + LLMLit** instalat și pregătit de utilizare local! Dacă ai întrebări, spune-mi. 😊
|
| 186 |
+
|
| 187 |
🔗 **Mai multe detalii:** [LLMLit on Hugging Face](https://huggingface.co/LLMLit) 🚀
|
| 188 |
|
| 189 |
|