Documentation Index
Fetch the complete documentation index at: https://docs.openclaas.com/llms.txt
Use this file to discover all available pages before exploring further.
Quick Start
Prerequisites
- Python 3.11+
- uv, fast Python package manager
- Docker (for Local and Tinker Docker deployments)
- GPU with >= 24 GB VRAM (Local backend only)
git clone https://github.com/kfallah/CLaaS.git
cd CLaaS
Choose your backend
Runs SDPO training and vLLM inference on your own GPU. Requires >= 24 GB VRAM.# Install dependencies
uv sync --extra local
# Docker setup (recommended)
cd docker
cp .env.local.example .env
# Edit .env -- set TELEGRAM_BOT_TOKEN
docker compose --profile local up --build
Full Local backend reference
Requirements, all config variables, services, and manual setup
Uses Tinker’s hosted inference and training. No GPU required.# Install dependencies
uv sync --extra tinker
# Docker setup
cd docker
cp .env.tinker.example .env.tinker
# Edit .env.tinker -- set TELEGRAM_BOT_TOKEN + TINKER_API_KEY
docker compose -f docker-compose.tinker.yml --env-file .env.tinker up --build
Full Tinker backend reference
Tinker config, model naming gotchas, and services
Runs distillation remotely on Modal (L40S GPUs). No local GPU needed.# Install dependencies
uv sync --extra local
# Authenticate with Modal
uv run modal token new
# Deploy
export HF_TOKEN=...
export CLAAS_BASE_MODEL_ID=Qwen/Qwen3-8B
uv run modal deploy -m claas.deploy
https://your-app--claas-distill-fastapi-app.modal.run.Full Modal backend reference
Deployment, config, and LoRA storage on Modal Volumes
Verify your setup
Once the stack is running, verify with:
# Check inference endpoint
curl http://localhost:8000/v1/models -H "Authorization: Bearer sk-local"
# Check CLaaS API
curl http://localhost:8080/
# List LoRA adapters
curl http://localhost:8080/v1/lora
