https://github.com/projectcontinuum/continuum-feature-ai
AI and ML features for continuum
https://github.com/projectcontinuum/continuum-feature-ai
ai continuum continuum-feature cuda llm ml mlops pytourch unsloth
Last synced: about 2 months ago
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AI and ML features for continuum
- Host: GitHub
- URL: https://github.com/projectcontinuum/continuum-feature-ai
- Owner: projectcontinuum
- Created: 2026-03-07T06:27:04.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-03-25T07:02:04.000Z (2 months ago)
- Last Synced: 2026-03-25T14:52:35.315Z (2 months ago)
- Topics: ai, continuum, continuum-feature, cuda, llm, ml, mlops, pytourch, unsloth
- Language: Python
- Homepage:
- Size: 156 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
---
## 🌐 Part of Project Continuum
This is the **AI/ML feature repository** for [Project Continuum](https://github.com/projectcontinuum/Continuum) — a distributed, crash-proof workflow execution platform. It provides nodes for training and fine-tuning large language models directly inside your visual workflows.
---
## 🔥 What Is This
A standalone Gradle project containing AI/ML workflow nodes. Currently features the **LLM Trainer (Unsloth)** node — fine-tune large language models using LoRA (Low-Rank Adaptation) with Unsloth acceleration, right inside your workflow graph.
Ships as a Spring Boot worker with an auto-managed Python virtual environment for ML execution.
---
## 🧪 Included Nodes
### LLM Trainer (Unsloth)
Fine-tune Large Language Models using LoRA with Unsloth acceleration.
| | |
|---|---|
| **Input** | Parquet table with instruction + response columns |
| **Output** | Model info — path to LoRA adapter weights, base model, training config |
| **Category** | Machine Learning, LLM Training |
**Supported Base Models:**
| Provider | Models |
|----------|--------|
| **Unsloth** (fastest) | Phi-4, Phi-4-mini-instruct, Mistral 7B, Llama 3/3.1/3.2, Gemma 2, Qwen 2.5 |
| **Microsoft** | Phi-2, Phi-3-mini-4k-instruct |
| **Meta** | Llama-2-7b, Llama-2-7b-chat |
| **Google** | Gemma 2B, Gemma 7B |
| **Qwen** | Qwen2-7B, Qwen2-7B-Instruct |
| **TII** | Falcon 7B, Falcon 7B-instruct |
| **Custom** | Any HuggingFace causal language model |
**Configurable Parameters:**
| Group | Parameters |
|-------|-----------|
| **Model** | Base model (HuggingFace ID), HuggingFace token for gated models |
| **Data** | Input column, output column, system prompt |
| **Training** | Epochs, batch size, learning rate, max sequence length, warmup steps, weight decay, gradient accumulation |
| **LoRA** | Rank (r), alpha, dropout |
| **Advanced** | 4-bit quantization, random seed, save steps, logging steps, Parquet batch size |
**Key Features:**
- Unsloth acceleration on Linux + CUDA (2x faster, 60% less memory)
- Falls back to standard HuggingFace transformers on other platforms
- 4-bit quantization for reduced memory usage
- Real-time training progress streaming via Kafka
- Auto-managed Python virtual environment
---
## 🐍 Python Environment
The Unsloth node executes training via a Python virtual environment that is **automatically created at startup** if missing.
| Setting | Default |
|---------|---------|
| `com.continuum.feature.ai.unsloth-trainer.venv-path` | `~/.continuum/unsloth-env` |
| `com.continuum.feature.ai.unsloth-trainer.cache-storage-path` | `./.continuum-cache/workflow-data` |
**Required Python packages** (auto-installed): pyarrow, pandas, datasets, torch, transformers, peft, trl, accelerate, hf_transfer, sentencepiece, protobuf, bitsandbytes, unsloth (Linux + CUDA only).
---
## 📦 Dependencies
Shared libraries from [Continuum](https://github.com/projectcontinuum/Continuum) via GitHub Packages:
| Dependency | Purpose |
|-----------|---------|
| `continuum-commons:0.0.1` | Base node model, data types, Parquet/S3 utilities |
| `continuum-worker-springboot-starter:0.0.1` | Worker framework — registers nodes with Temporal |
---
## 🚀 Quick Start
### Prerequisites
- **JDK 21** — [Eclipse Temurin](https://adoptium.net/) recommended
- **Python 3.10+** — For the Unsloth training environment
- **Docker & Docker Compose** — For local infrastructure
- **GitHub PAT** with `read:packages` scope
- **(Optional) CUDA GPU** — For Unsloth acceleration
Set environment variables:
```bash
export GITHUB_USERNAME=your-github-username
export GITHUB_TOKEN=ghp_your-personal-access-token
```
### Run
```bash
# Start infrastructure (Temporal, Kafka, MinIO, API Server, Message Bridge)
cd docker && docker compose up -d
# Build
./gradlew build
# Start the AI worker (auto-creates Python venv on first run)
./gradlew :worker:bootRun
```
---
## 📁 Project Structure
```
continuum-feature-ai/
├── features/
│ └── continuum-feature-unsloth/ # Unsloth LLM trainer node
│ ├── build.gradle.kts # Depends on continuum-commons
│ └── src/main/kotlin/.../
│ ├── AutoConfigure.kt # Spring auto-configuration
│ ├── node/
│ │ └── UnslothTrainerNodeModel.kt
│ └── python/
│ └── PythonEnvironmentManager.kt
├── worker/ # Spring Boot worker application
│ ├── build.gradle.kts # Depends on starter + unsloth feature
│ └── src/main/
│ ├── kotlin/.../App.kt
│ └── resources/application.yaml
├── docker/ # Local development infrastructure
│ └── docker-compose.yml
├── settings.gradle.kts
├── gradle.properties
└── README.md
```
---
## 🗺️ Roadmap
- [x] LLM fine-tuning with Unsloth + LoRA
- [x] Auto-managed Python virtual environment
- [x] 4-bit quantization support
- [ ] Inference node — run inference against fine-tuned or base models
- [ ] Model evaluation node — automated benchmarking
- [ ] Multi-GPU training support
- [ ] More model architectures (vision, embedding)
---
## 🔗 Related Repositories
| Repository | Description |
|-----------|-------------|
| [Continuum](https://github.com/projectcontinuum/Continuum) | Core backend — API server, worker framework, shared libraries |
| [continuum-workbench](https://github.com/projectcontinuum/continuum-workbench) | Browser IDE — Eclipse Theia + React Flow workflow editor |
| [continuum-feature-base](https://github.com/projectcontinuum/continuum-feature-base) | Base analytics nodes — data transforms, REST, scripting, anomaly detection |
| **continuum-feature-ai** (this repo) | AI/ML nodes — LLM fine-tuning with Unsloth + LoRA |
| [continuum-feature-template](https://github.com/projectcontinuum/continuum-feature-template) | Template — scaffold your own custom worker with nodes |