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license: mit
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# 🚀 IoraX 3B — Efficient Conversational AI Model
## ✨ 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. |