Instructions to use PyThaGo/LitSeekR1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PyThaGo/LitSeekR1 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/LitSeekR1 # Run inference directly in the terminal: llama cli -hf PyThaGo/LitSeekR1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PyThaGo/LitSeekR1 # Run inference directly in the terminal: llama cli -hf PyThaGo/LitSeekR1
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/LitSeekR1 # Run inference directly in the terminal: ./llama-cli -hf PyThaGo/LitSeekR1
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/LitSeekR1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf PyThaGo/LitSeekR1
Use Docker
docker model run hf.co/PyThaGo/LitSeekR1
- LM Studio
- Jan
- Ollama
How to use PyThaGo/LitSeekR1 with Ollama:
ollama run hf.co/PyThaGo/LitSeekR1
- Unsloth Desktop
- Docker Model Runner
How to use PyThaGo/LitSeekR1 with Docker Model Runner:
docker model run hf.co/PyThaGo/LitSeekR1
- Lemonade
How to use PyThaGo/LitSeekR1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PyThaGo/LitSeekR1
Run and chat with the model
lemonade run user.LitSeekR1-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Cristian Sas commited on
Update README.md
Browse files
README.md
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---
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---
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# **📌 Installation Guide: Ollama + LitSeek**
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## **🔹 Step 1: Install Ollama**
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Ollama is a lightweight framework for running large language models (LLMs) locally.
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### **🖥️ For macOS & Linux**
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1️⃣ **Open a terminal and run:**
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```sh
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curl -fsSL https://ollama.com/install.sh | sh
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```
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2️⃣ **Restart your terminal.**
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### **🖥️ For Windows (WSL2 required)**
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1️⃣ **Enable WSL2 and install Ubuntu:**
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- Open PowerShell as Administrator and run:
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```powershell
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wsl --install
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```
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- Restart your computer.
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2️⃣ **Install Ollama inside WSL2:**
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```sh
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curl -fsSL https://ollama.com/install.sh | sh
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```
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3️⃣ **Check if Ollama is installed correctly:**
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```sh
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ollama
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```
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If it prints the usage instructions, the installation is successful. 🎉
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---
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## **🔹 Step 2: Install LLMLit from Hugging Face**
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LLMLit can be downloaded and run inside Ollama using the `ollama pull` command.
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1️⃣ **Open a terminal and run:**
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```sh
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ollama pull llmlit/LeetSeek-R1-DLlama-8B
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```
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2️⃣ **Verify the installation:**
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```sh
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ollama list
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```
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You should see **LLMLit** in the list of installed models. ✅
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---
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## **🔹 Step 3: Run LLMLit in Ollama**
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After installation, you can interact with **LLMLit** using:
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```sh
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ollama run llmlit/LeetSeek-R1-DLlama-8B
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```
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This starts a local session where you can chat with the model! 🤖
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For custom prompts:
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```sh
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ollama run llmlit/LeetSeek-R1-DLlama-8B "Hello, how can I use LLMLit?"
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```
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---
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## **🔹 Bonus: Use LLMLit in Python**
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If you want to integrate **LLMLit** into a Python script, install the required library:
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```sh
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pip install ollama
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```
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Then, create a Python script:
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```python
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import ollama
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response = ollama.chat(model='llmlit/LeetSeek-R1-DLlama-8B', messages=[{'role': 'user', 'content': 'How does LLMLit work?'}])
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print(response['message']['content'])
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```
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---
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🚀 **Done!** Now you have **Ollama + LLMLit** installed and ready to use locally!
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