# LLM Trainer

A web-based interface for building fine-tuning datasets and training LLMs on a remote GPU server. The frontend connects to a FastAPI backend that SSHes into your GPU machine, runs the [synthetic-data-kit](https://github.com/anthropics/synthetic-data-kit) pipeline, and streams live output back to the browser.

## Architecture

```
Browser (React/Vite)
    │
    ▼
FastAPI Backend (Docker, port 8080)
    │  REST + WebSocket
    ▼
Remote GPU Server (SSH)
    ├── synthetic-data-kit  →  parse / generate / curate / export
    └── train.py            →  fine-tuning run
```

Ollama (port 11434 on the GPU server) is used for model management — pulling, listing, and deleting models.

## Pipeline stages

| Stage | Directory | Description |
|-------|-----------|-------------|
| `input` | `/opt/synthetic/…/data/input` | Raw source documents |
| `parsed` | `/opt/synthetic/…/data/parsed` | Ingested plain text |
| `generated` | `/opt/synthetic/…/data/generated` | Raw QA pairs |
| `curated` | `/opt/synthetic/…/data/curated` | Filtered pairs (quality threshold) |
| `final` | `/opt/synthetic/…/data/final` | Export-ready JSONL/CSV |

## Getting started

### Prerequisites

- Docker + Docker Compose
- A remote machine with:
  - SSH access
  - `miniconda3` with a `synthetic-data` conda env containing `synthetic-data-kit`
  - `train.py` at `/opt/synthetic/train.py`
  - Ollama running on port `11434`

### Run

```bash
docker compose up --build
```

| Service | URL |
|---------|-----|
| Frontend | http://localhost:3000 |
| Backend API | http://localhost:8080 |
| API docs | http://localhost:8080/docs |

The `OLLAMA_URL` environment variable in `docker-compose.yml` defaults to `http://192.168.2.47:11434` — update it to point to your GPU server.

### Configuration

The pipeline reads its config from `/opt/synthetic/synthetic-data-kit/config.yaml` on the remote server. You can edit it live from the **Config Editor** tab in the UI.

## Project structure

```
├── backend/
│   ├── main.py          # FastAPI app — all REST and WebSocket endpoints
│   ├── pipeline.py      # Command builders for synthetic-data-kit stages
│   ├── ssh_client.py    # Paramiko SSH manager (connect, stream, upload, shell)
│   ├── gpu.py           # nvidia-smi GPU stats
│   ├── requirements.txt
│   └── Dockerfile
├── frontend/
│   ├── src/
│   │   ├── App.jsx
│   │   └── components/
│   │       ├── ConnectionPanel.jsx   # SSH connect / GPU status
│   │       ├── DocumentManager.jsx   # Upload & browse pipeline files
│   │       ├── PipelineRunner.jsx    # Run ingest → create → curate → save
│   │       ├── QAPairViewer.jsx      # Preview generated QA pairs
│   │       ├── TrainingMonitor.jsx   # Launch training, live log stream
│   │       ├── ModelManager.jsx      # Pull / delete Ollama models
│   │       ├── ConfigEditor.jsx      # Edit remote config.yaml
│   │       └── Terminal.jsx          # Interactive SSH terminal (xterm.js)
│   └── Dockerfile
├── packaging/
│   └── build-deb.sh     # Build a .deb installer
└── docker-compose.yml
```

## API reference

Key endpoints (full docs at `/docs`):

| Method | Path | Description |
|--------|------|-------------|
| `POST` | `/api/connect` | Open SSH connection |
| `GET` | `/api/status` | Connection + GPU status |
| `GET` | `/api/files/{stage}` | List files at a pipeline stage |
| `POST` | `/api/upload` | Upload a file to the `input` stage |
| `WS` | `/api/pipeline/ingest` | Stream ingest (parse) output |
| `WS` | `/api/pipeline/create` | Stream QA pair generation |
| `WS` | `/api/pipeline/curate` | Stream curation / filtering |
| `WS` | `/api/pipeline/save` | Stream export to JSONL/CSV |
| `WS` | `/api/train` | Stream fine-tuning run |
| `WS` | `/api/terminal` | Interactive SSH shell |
| `GET` | `/api/models` | List Ollama models |
| `WS` | `/api/models/pull` | Pull an Ollama model |