---
base_model: ErebusTN/EGen-SA1Q8
model_name: EGen-SA1Q8
tags:
- base_model:adapter:ErebusTN/EGen-SA1Q8
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
license: apache-2.0
datasets:
- ErebusTN/EGen-Dataset
- OpenLeecher/lmsys_chat_1m_clean
- lmsys/lmsys-chat-1m
- open-r1/codeforces-cots
- newfacade/LeetCodeDataset
- ise-uiuc/Magicoder-OSS-Instruct-75K
library_name: peft
---

<div align="center">
  
<h1 style="font-size: 2em;">🏛️ Athena Project 🐼</h1>
  
**Next-generation Supervised Fine-Tuning (SFT) for advanced reasoning and language understanding.**
<img src="https://i.ibb.co/5X8LK2TQ/ATHENA-PROJECT.png" alt="Athena Project" width="500" height="600">
[Explore Model](https://huggingface.co/ErebusTN/EGen-SA1Q8) • [Report Bug](https://www.google.com/search?q=https://huggingface.co/ErebusTN/EGen-SA1Q8/discussions) • [ErebusTN Profile](https://huggingface.co/ErebusTN)<br>
The model was trained and validated using a cutting-edge software stack to ensure stability and performance:<br>
![Transformers](https://img.shields.io/badge/Transformers-4.54.0-yellow?logo=huggingface&logoColor=white)
![PyTorch](https://img.shields.io/badge/PyTorch-2.9.0+cu126-red?logo=pytorch&logoColor=white)
![PEFT](https://img.shields.io/badge/PEFT-0.18.0-yellow?logo=huggingface&logoColor=white)
![TRL](https://img.shields.io/badge/TRL-0.20.0-black?logo=huggingface&logoColor=white)
![Datasets](https://img.shields.io/badge/Datasets-EGen_Dataset-red?logo=huggingface&logoColor=white)
![Tokenizers](https://img.shields.io/badge/Tokenizers-0.21.4-yellow?logo=huggingface&logoColor=white)
</div>

---

## 📖 Overview

The **Athena Project (2025)** represents a milestone in efficient high-performance language modeling. Developed by **ErebusTN**, the **EGen-SA1Q8** variant is a precision-tuned model designed to deliver superior conversational capabilities and structured data processing.

By leveraging **Supervised Fine-Tuning (SFT)**, Athena has been optimized to follow complex instructions with high fidelity, maintaining a balance between creative generation and factual accuracy.

## 🚀 Key Features

* **SFT Optimized:** Trained using Supervised Fine-Tuning to ensure alignment with human intent.
* **2025 Architecture:** Incorporates the latest advancements in transformer optimization.
* **Quantization Ready:** The SA1Q8 designation signifies optimized weight distribution for efficient deployment.
* **High Compatibility:** Seamlessly integrates with the modern Hugging Face ecosystem.

## 🛠️ Tech Stack & Frameworks





---

## 💻 Quick Start

You can load the model using the following snippet:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ErebusTN/EGen-SA1Q8"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Explain the significance of the Athena Project in 2025."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---

## 📊 Training Methodology

Athena Project utilized the **SFT (Supervised Fine-Tuning)** trainer from the `TRL` library. This process involved:

1. **Instruction Following:** Tuning on high-quality, human-annotated datasets.
2. **Parameter Efficiency:** Utilizing `PEFT` for optimized memory usage during the tuning phase.
3. **Precision Alignment:** Leveraging the latest `cu126` CUDA kernels for accelerated compute.

## 🤝 Contact & Support

**Developed by ErebusTN**

* **Hugging Face:** [@ErebusTN](https://huggingface.co/ErebusTN)
* **Github** [@EGen-V](https://github.com/EGen-V)
* **Project Year:** 2025

---

<div align="center">
<i>Built with ❤️ By ErebusTN.</i>
</div>