readme update

Author: ProfEngel

readme.md

@@ -49,7 +49,7 @@ With the OTW-Viewer, all generated documents (converted Markdown files, lexicon
 - 📱 **No-Code Gradio Interface**: Drag-&-drop upload with live terminal and complete pipeline control
 - 🌐 **Multi-Format Export**: LoRA, Merged (both for transformers, vLLM, etc.), GGUF in Q_8 with quantizations for local deployment (OpenWebUI/LM-Studio)
 - 🔍 **VLM Integration**: Vision-Language-Models for automatic image descriptions in documents
-- ⚡ **Universal API Support**: Works with OpenAI, OpenRouter, Ollama, LM Studio, and any OpenAI-compatible API
+- ⚡ **Runpod Integration**: Scalable cloud GPU support for cost-effective training
 
 ***
 
@@ -60,20 +60,13 @@ With the OTW-Viewer, all generated documents (converted Markdown files, lexicon
 **Hardware:**
 - **Linux system recommended** (Ubuntu 22.04 LTS or similar)
 - **At least 100 GB free storage space**
-- **For Training: NVIDIA GPU with at least 20 GB VRAM** (depending on the model being trained)
+- **NVIDIA GPU with at least 20 GB VRAM** (depending on the model being trained)
   - RTX 4090/A6000/A100 recommended
   - For smaller models: RTX 3090/4080 (16GB) possible
-- **For Dataset Generation Only: No GPU required** (can use cloud APIs)
-- **CUDA 12.8+ and cuDNN** (only if using local GPU)
+- **CUDA 12.8+ and cuDNN installed**
 
 **Accounts:**
 - **HuggingFace Account** with Access Token (Read + optional Write)
-- **API Access** (choose one):
-  - OpenAI API Key
-  - OpenRouter API Key
-  - Ollama (local installation)
-  - LM Studio (local installation)
-  - Any OpenAI-compatible API endpoint
 
 ### HuggingFace Token Setup
 
@@ -82,222 +75,78 @@ With the OTW-Viewer, all generated documents (converted Markdown files, lexicon
 3. Create a new token with **Read** permission (and **Write** for model upload)
 4. Note down the token for installation
 
-### Universal Installation (NEW - Works with any API)
-
-OpenTuneWeaver now supports **any OpenAI-compatible API** for dataset generation. Choose your preferred installation method:
-
-#### Quick Installation with Direct Script
-
-```bash
-# Download and run the universal setup script
-wget https://raw.githubusercontent.com/ProfEngel/OpenTuneWeaver/main/setup_universal.sh
-chmod +x setup_universal.sh
-
-# Configure your API (choose one):
-
-# Option 1: For OpenAI
-export OPENAI_API_TYPE=openai
-export OPENAI_API_BASE=https://api.openai.com/v1
-export OPENAI_API_KEY=sk-your-key-here
-export OPENAI_MODEL_NAME=gpt-4
-
-# Option 2: For OpenRouter
-export OPENAI_API_TYPE=openrouter
-export OPENAI_API_BASE=https://openrouter.ai/api/v1
-export OPENAI_API_KEY=your-openrouter-key
-export OPENAI_MODEL_NAME=meta-llama/llama-3.2-3b-instruct
-
-# Option 3: For local Ollama (default)
-export OPENAI_API_TYPE=ollama
-export OPENAI_API_BASE=http://localhost:11434/v1
-export OPENAI_MODEL_NAME=gemma3:12b-it-qat #VLM-Model for Image description
-
-# Option 4: For LM Studio
-export OPENAI_API_TYPE=lmstudio
-export OPENAI_API_BASE=http://localhost:1234/v1
-export OPENAI_MODEL_NAME=your-loaded-model
-
-# Run the installation
-./setup_universal.sh
-```
-
-#### Installation with Virtual Environment (Recommended)
-
-```bash
-# Create and activate virtual environment
-python3 -m venv opentuneweaver-env
-source opentuneweaver-env/bin/activate
-
-# Clone repository
-git clone https://github.com/ProfEngel/OpenTuneWeaver.git
-cd OpenTuneWeaver
-
-# Install dependencies
-pip install --upgrade pip
-pip install -r requirements.txt
-
-# Configure API (see options above)
-export OPENAI_API_TYPE=openai  # or your preferred API
-export OPENAI_API_BASE=https://api.openai.com/v1
-export OPENAI_API_KEY=your-api-key
-export OPENAI_MODEL_NAME=gpt-4
-
-# Run setup
-./setup_universal.sh
-```
-
-#### Installation with Conda
-
-```bash
-# Create conda environment
-conda create -n opentuneweaver python=3.11
-conda activate opentuneweaver
-
-# Clone repository
-git clone https://github.com/ProfEngel/OpenTuneWeaver.git
-cd OpenTuneWeaver
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Install unsloth (for training)
-pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
-
-# Configure API (see options above)
-export OPENAI_API_TYPE=your-api-type
-export OPENAI_API_BASE=your-api-base-url
-export OPENAI_API_KEY=your-api-key
-export OPENAI_MODEL_NAME=your-model
-
-# Run setup
-./setup_universal.sh
-```
-
-#### Docker Installation (Recommended for Production) (not tested yet)
-
-```bash
-# Clone repository
-git clone https://github.com/ProfEngel/OpenTuneWeaver.git
-cd OpenTuneWeaver
-
-# Copy and configure environment
-cp .env.example .env
-# Edit .env with your API settings
-
-# Build and run with Docker Compose
-docker-compose up -d
-
-# Access at http://localhost:8080
-```
-
-### Runpod Installation (For Simple Online-GPU Training)
+### Quick Start with Runpod (Recommended)
 
 **Runpod Template:**
 ```
+
 runpod/pytorch:2.8.0-py3.11-cuda12.8.1-cudnn-devel-ubuntu22.04
 Disk Volume: 100 GB
 Pod Volume:  100 GB
 Open Ports: 8080,11434
+
 ```
 
 **Installation:**
-```bash
+```
+
 cd /workspace
 git clone https://github.com/ProfEngel/OpenTuneWeaver.git
-cd OpenTuneWeaver
-
-# For Runpod with Ollama (local inference)
+cp OpenTuneWeaver/setup_runpod_direct.sh .
+chmod +x setup_runpod_direct.sh
 ./setup_runpod_direct.sh
 
-# OR for Runpod with external API
-export OPENAI_API_TYPE=openai
-export OPENAI_API_BASE=https://api.openai.com/v1
-export OPENAI_API_KEY=your-key
-export OPENAI_MODEL_NAME=gpt-4
-./setup_universal.sh
 ```
 
-### API Configuration Examples
+**After installation:**
 
-#### Using OpenAI GPT-4
-```bash
-export OPENAI_API_TYPE=openai
-export OPENAI_API_BASE=https://api.openai.com/v1
-export OPENAI_API_KEY=sk-...your-key...
-export OPENAI_MODEL_NAME=gpt-5-mini  # or gpt-4
-```
+wait until the installation is done, then press y for starting the ui. The ui starts on port http://yourIP:8080
 
-#### Using OpenRouter
-```bash
-export OPENAI_API_TYPE=openrouter
-export OPENAI_API_BASE=https://openrouter.ai/api/v1
-export OPENAI_API_KEY=your-openrouter-key
-export OPENAI_MODEL_NAME=meta-llama/llama-3.2-3b-instruct
-# Other models: claude-3-opus, mistral-large, etc.
-```
+In Runpod access via Runpod web interface on port 8080.
 
-#### Using Local Ollama
-```bash
-# First install Ollama
-curl -fsSL https://ollama.com/install.sh | sh
-ollama pull gemma3:12b-it-qat
+### Alternative Installation Methods
 
-# Configure OpenTuneWeaver
-export OPENAI_API_TYPE=ollama
-export OPENAI_API_BASE=http://localhost:11434/v1
-export OPENAI_MODEL_NAME=gemma3:12b-it-qat
+**Docker Installation:** *(Coming Soon)*
 ```
 
-#### Using LM Studio
-```bash
-# Start LM Studio and load a model
-# Then configure:
-export OPENAI_API_TYPE=lmstudio
-export OPENAI_API_BASE=http://localhost:1234/v1
-export OPENAI_MODEL_NAME=your-loaded-model
-```
+docker run -d -p 7860:7860 --gpus all -v opentuneweaver:/app/data --name opentuneweaver opentuneweaver/opentuneweaver:latest
 
-#### Using Custom API Endpoint
-```bash
-export OPENAI_API_TYPE=custom
-export OPENAI_API_BASE=https://your-api-endpoint.com/v1
-export OPENAI_API_KEY=your-api-key
-export OPENAI_MODEL_NAME=your-model-name
 ```
 
-### Starting OpenTuneWeaver
+**Conda Installation:**
+```
 
-After installation, start the application:
+conda create -n opentuneweaver python=3.11
+conda activate opentuneweaver
+apt-get update && apt-get upgrade -y
+git clone https://github.com/ProfEngel/OpenTuneWeaver.git
+cp OpenTuneWeaver/setup_runpod_direct.sh .
+chmod +x setup_runpod_direct.sh
 
-```bash
-# Direct start
-./start_otw.sh
+# Installation von unsloth_zoo direkt von GitHub
+pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
 
-# Or with custom port
-export SERVER_PORT=7860
-./start_otw.sh
+# Dann das Setup-Skript ausführen
+./setup_runpod_direct.sh
 
-# Access the UI
-# Local: http://localhost:8080
-# Remote: http://your-server-ip:8080
 ```
 
-### Troubleshooting
+**Virtual Environment:**
+```
 
-If you encounter issues:
+python3.11 -m venv opentuneweaver-env
+source opentuneweaver-env/bin/activate
+apt-get update && apt-get upgrade -y
+git clone https://github.com/ProfEngel/OpenTuneWeaver.git
+cp OpenTuneWeaver/setup_runpod_direct.sh .
+chmod +x setup_runpod_direct.sh
 
-```bash
-# Check installation
-./debug_otw.sh
+# Installation von unsloth_zoo direkt von GitHub
+pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
 
-# View logs
-tail -f logs/pipeline.log
+# Dann das Setup-Skript ausführen
+./setup_runpod_direct.sh
 
-# Test API connection
-curl -X POST $OPENAI_API_BASE/chat/completions \
-  -H "Authorization: Bearer $OPENAI_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"model": "'$OPENAI_MODEL_NAME'", "messages": [{"role": "user", "content": "Test"}]}'
 ```
 
 ***
@@ -367,15 +216,17 @@ OpenTuneWeaver would not be possible without these excellent open-source framewo
 
 If you use OpenTuneWeaver in your research, please cite our paper:
 
-```bibtex
+```
+
 @article{opentuneweaver2024,
-  title={OpenTuneWeaver: Semantically-structured, Curatable LLM Fine-tuning Pipeline for Research and Education},
-  author={Engel, Prof. Dr. Mathias},
-  journal={arXiv preprint},
-  year={2024},
-  institution={Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen},
-  note={Funded by MWK Baden-Württemberg and Stifterverband Deutschland}
+title={OpenTuneWeaver: Semantically-structured, Curatable LLM Fine-tuning Pipeline for Research and Education},
+author={Engel, Prof. Dr. Mathias},
+journal={arXiv preprint},
+year={2024},
+institution={Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen},
+note={Funded by MWK Baden-Württemberg and Stifterverband Deutschland}
 }
+
 ```
 
 **Paper available:**
@@ -427,4 +278,4 @@ Semantically-structured, curatable all-in-one LLM fine-tuning pipeline
 
 ### Topics
 
-`llm` `finetuning` `ai` `machine-learning` `nlp` `semantic-chunking` `lora` `qlora` `pdf-processing` `qa-generation` `benchmarking` `gradio` `huggingface` `educational-ai` `research-tools`
+`llm` `finetuning` `ai` `machine-learning` `nlp` `semantic-chunking` `lora` `qlora` `pdf-processing` `qa-generation` `benchmarking` `gradio` `huggingface` `educational-ai` `research-tools`