---
license: mit
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# 🚀 IoraX 3B — Efficient Conversational AI Model
![IoraX Logo](./IoraX.png)


## ✨ Model Overview

**IoraX 3B** is a highly efficient 3-billion parameter Transformer, fine-tuned using LoRA adapters on Meta LLaMA 3.2 (3B) — with 4-bit quantization to keep it lightning fast and lightweight! 

This model specializes in deep conversational understanding, logical reasoning, and coherent long-form generation — your AI companion for research, education, and creative tasks.

---

## 🎯 Features & Capabilities

- 🧠 **Size:** 3B parameters  
- ⚙️ **Base:** Meta LLaMA 3.2 (3B)  
- 🔧 **Fine-tuning:** LoRA with 4-bit quantization  
- ⏳ **Max context length:** 2048 tokens (with RoPE scaling)  
- 📚 **Training data:** Blend of public conversational datasets + expert-curated Q&A  
- 🔄 **Epochs:** 3 for balanced speed and learning  
- 🌍 **Language:** English  

---

## 🚀 Use Cases

| Use Case                | Description                               |  
|------------------------|-----------------------------------------|  
| 💬 Conversational AI   | Customer support, chatbots, assistants  |  
| 🎓 Education           | Tutoring, concept explanation, Q&A      |  
| 🧪 Research Assistant  | Drafting, summarizing, brainstorming     |  
| ✍️ Creative Writing    | Storytelling, script generation          |  

---

## ⚠️ Limitations

- 📅 **Knowledge cutoff:** Data up to 2023 only  
- ⚖️ **Bias:** May reflect biases present in the training corpus  
- ✔️ **Accuracy:** Verify important outputs, especially in critical domains  
- 🧑‍⚖️ **Not a replacement for experts:** Use responsibly  

---

## 💡 Quick Start

```python
from transformers import AutoTokenizer
from unsloth import FastLanguageModel

model_name = "XythicK/IoraX-3B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = FastLanguageModel.from_pretrained(model_name, load_in_4bit=True, max_seq_length=2048)

messages = [
    {"role": "user", "content": "Explain the philosophical significance of the Eiffel Tower. 🌉🤔"}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    tokenize=True, 
    add_generation_prompt=True, 
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    input_ids=inputs, 
    max_new_tokens=128, 
    temperature=1.2, 
    use_cache=True
)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```

## 🙋 Contact

**Maintainer:** **M Mashhudur Rahim [XythicK]**
 
**Role:**  
**Independent Machine Learning Researcher & Model Infrastructure Maintainer** 

(Focused on model quantization, optimization, and efficient deployment)


For issues, improvement requests, or additional quantization formats, please use the Hugging Face Discussions or Issues tab.

## 📄 Citation

If you use IoraX in your work, please cite:
```bibtex
@misc{ioraX2025,
  title = {IoraX 3B: Efficient Conversational AI},
  author = {M Mashhudur Rahim (XythicK)},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/XythicK/IoraX-3B}}
}
```

## ❤️ Acknowledgements

Thanks to Hugging Face and the open-source machine learning community for providing the tools and platforms that make efficient model sharing and deployment possible.