---
language: en
license: apache-2.0
tags:
  - intellite
  - "small-language-model"
  - transformers
  - pytorch
  - conversational-ai
  - huggingface
  - low-resource
---

# IntellIte Chat

IntellIte Chat is a lightweight conversational AI model (~45M parameters) designed for warm, engaging dialogue and basic reasoning. Part of the *IntellIte* series, it delivers efficient performance on modest hardware, complete with streaming data loading, episodic memory buffers, and RAG-based knowledge augmentation.

---

## ⚙️ Key Features

- **Small & Efficient**: ~45M parameters, ideal for edge devices and academic projects.  
- **Streaming Data**: Uses Hugging Face `IterableDataset` for on-the-fly data without local storage constraints.  
- **Memory Buffer**: Maintains the last 200 messages for coherent multi-turn conversations.  
- **RAG Integration**: FAISS-based retrieval for up-to-date knowledge augmentation.  
- **Content Safety**: Built-in filters to enforce conversational guidelines.  
- **Extensible API**: Hook into `generate_with_plugins()` for custom prompts or downstream tasks.

---

## 💾 Installation

```bash
pip install transformers datasets faiss-cpu torch huggingface-hub
```

---

## 🚀 Quick Start

```python
from il import generate_with_plugins

response = generate_with_plugins(
  prompt="Hello, how's it going?",
  source="wiki",
  k=3,
  max_new_tokens=100
)
print(response)
```

---

## 🛠️ Training Pipeline

Run the main training script:

```bash
export HF_TOKEN=<your_hf_token>
python il.py --hf_token $HF_TOKEN --seed 42
```

The script will:

1. Stream Wikipedia, CodeParrot, and grade-school math datasets.  
2. Apply cosine LR scheduling, weight decay, and label smoothing.  
3. Run simple evals (2 chat, 1 code prompt) at each epoch end.  
4. Save & push the best model to `ProCreations/IntellIte` on Hugging Face.

---

## 📊 Evaluation & Monitoring

A `SimpleEvalCallback` runs designated chat/code prompts each epoch, logging outputs for quick sanity checks.

---

## 🔧 Configuration Options

Edit `il.py` to customize:

- **Batch Sizes, LR, Scheduler** via `TrainingArguments`.  
- **Retrieval Sources**: adjust `k` and index sources.  
- **Memory Buffer**: change size or filter rules.

---

## 🌱 Fine‑Tuning on Custom Data

1. Prepare your dataset as a Hugging Face `Dataset` or `IterableDataset`.  
2. Interleave with base streams and pass to the Trainer.  
3. Use `--resume_from_checkpoint` to continue an interrupted run.

---

## 🤝 Contributing

Contributions welcome! Steps:

1. Fork the repo.  
2. Create a feature branch.  
3. Submit a PR with clear descriptions and tests.

---

## 📜 License

This project is licensed under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).

---

❤️ Developed by ProCreations under the *IntellIte* brand.