Update README.md

README.md

@@ -1,5 +1,115 @@
----
-license: other
-license_name: procreations-development
-license_link: LICENSE
----
+
+---
+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.
+