|
|
--- |
|
|
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. |
