ErebusTN/EGen-SA1Q8

🏛️ Athena Project 🐼

Next-generation Supervised Fine-Tuning (SFT) for advanced reasoning and language understanding. Explore Model • Report Bug • ErebusTN Profile
The model was trained and validated using a cutting-edge software stack to ensure stability and performance:

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📖 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

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💻 Quick Start

You can load the model using the following snippet:

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))

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📊 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
• Github @EGen-V
• Project Year: 2025

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Built with ❤️ By ErebusTN.