README.md

---
license: apache-2.0
language:
- ru
- en
- multilingual
tags:
- mistral
- russian
- english
- code
- machine-learning
- nlp
- transformer
- gqa
- rmsnorm
- swiglu
- rope
- flash-attention-2
- dark-ultima
- 5tb
- ultra-large
- experimental
- sharded
pipeline_tag: text-generation
size_categories: 5TB
---

# 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.

```python
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](https://github.com/MagistrTheOne/Radon2BMistral)
- Hugging Face: [MagistrTheOne/RadonDarkUltima](https://huggingface.co/MagistrTheOne/RadonDarkUltima)
- Creator: [MagistrTheOne](https://github.com/MagistrTheOne)

## License

Apache 2.0 License

## Citation

```bibtex
@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.