RadonDarkUltima (5TB) - Ultra-Large Scale Model

Model Description

RadonDarkUltima is an experimental 5TB parameter ultra-large scale Mistral-based transformer model designed for cutting-edge research and development. This model represents the pinnacle of the RADON ecosystem, pushing the boundaries of what's possible with open-source language models.

⚠️ EXPERIMENTAL MODEL - RESEARCH USE ONLY

This model is in experimental stage and requires massive computational resources. The framework is prepared but actual weights will be uploaded separately.

Key Features

Parameters: 2.5T parameters (2,500,000,000,000)
Architecture: Mistral with Llama 3 innovations (GQA, RMSNorm, SwiGLU, RoPE)
Context Length: 32,768 tokens (32K)
Languages: Russian, English, Code, Multilingual
Sharding: 100 shards of ~50GB each
Quantization: FP16 + INT8 hybrid for memory efficiency

Technical Specifications

Hidden Size: 16,384
Layers: 200
Attention Heads: 128
KV Heads: 16 (GQA ratio 8:1)
Intermediate Size: 65,536
Vocabulary: 256,000 tokens
Memory: ~5TB (FP16)

Hardware Requirements

Minimum Requirements

GPU: 5TB+ VRAM (A100 x64+ or H100 x32+)
RAM: 10TB+ system memory
Storage: 15TB+ NVMe SSD
Network: High-speed connection for shard loading

Recommended Setup

GPU: 10TB+ VRAM (H100 x64+ or equivalent)
RAM: 20TB+ system memory
Storage: 20TB+ NVMe SSD
Infrastructure: Data center with high-speed networking

Sharding Strategy

The model is split into 100 shards for efficient loading:

Shard 1: Embeddings (256,000 x 16,384)
Shards 2-99: Transformer layers (200 layers distributed)
Shard 100: Final layer norm + LM head

Each shard is approximately 50GB in size.

Usage (Framework Only)

⚠️ Note: This repository contains only the model framework. Actual weights will be uploaded separately.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model framework (weights not included)
model = AutoModelForCausalLM.from_pretrained(
    "MagistrTheOne/RadonDarkUltima",
    torch_dtype=torch.float16,
    device_map="auto",
    low_cpu_mem_usage=True
)

tokenizer = AutoTokenizer.from_pretrained("MagistrTheOne/RadonDarkUltima")

# Generate text (requires actual weights)
prompt = "Привет! Как дела?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Architecture

RadonDarkUltima (5TB parameters)
├── Mistral Base Architecture
├── Llama 3 Innovations
│   ├── Grouped Query Attention (GQA) - 8:1 ratio
│   ├── RMSNorm Layer Normalization
│   ├── SwiGLU Activation
│   └── Rotary Position Embeddings (RoPE)
├── Flash Attention 2
├── Gradient Checkpointing
├── Sharded Weights (100 shards)
├── FP16 + INT8 Hybrid Quantization
└── Ultra-Large Scale Optimization

Performance Expectations

This experimental model is designed for:

Ultra-long context processing (32K+ tokens)
Advanced reasoning and problem-solving
Multilingual understanding (Russian, English, Code)
Research applications requiring massive scale
Benchmarking against largest commercial models

Limitations

Experimental: Not production-ready
Massive resources: Requires data center infrastructure
Weights pending: Framework only, weights uploaded separately
Research use: Intended for research and development
High cost: Significant computational requirements

Creator

MagistrTheOne - Creator and lead developer of RADON

Specialized in ultra-large scale AI models
Focus on Russian-English machine learning applications
Open-source AI advocate and researcher
Creator of the RADON ecosystem

Contact

GitHub: MagistrTheOne/Radon2BMistral
Hugging Face: MagistrTheOne/RadonDarkUltima
Creator: MagistrTheOne

License

Apache 2.0 License

Citation

@misc{radon-dark-ultima-2024,
  title={RadonDarkUltima: 5TB Parameter Ultra-Large Scale Mistral-based Transformer},
  author={MagistrTheOne},
  year={2024},
  url={https://huggingface.co/MagistrTheOne/RadonDarkUltima}
}

Created with ❤️ by MagistrTheOne
Pushing the boundaries of open-source AI! 🚀

Warning

This is an experimental research model requiring massive computational resources. Use responsibly and only for research purposes.

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