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1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4# Load model framework (weights not included)
5model = AutoModelForCausalLM.from_pretrained(
6 "MagistrTheOne/RadonDarkUltima",
7 torch_dtype=torch.float16,
8 device_map="auto",
9 low_cpu_mem_usage=True
10)
11
12tokenizer = AutoTokenizer.from_pretrained("MagistrTheOne/RadonDarkUltima")
13
14# Generate text (requires actual weights)
15prompt = "Привет! Как дела?"
16inputs = tokenizer(prompt, return_tensors="pt")
17outputs = model.generate(**inputs, max_length=100, temperature=0.7)
18response = tokenizer.decode(outputs[0], skip_special_tokens=True)
19print(response)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 Optimization1@misc{radon-dark-ultima-2024,
2 title={RadonDarkUltima: 5TB Parameter Ultra-Large Scale Mistral-based Transformer},
3 author={MagistrTheOne},
4 year={2024},
5 url={https://huggingface.co/MagistrTheOne/RadonDarkUltima}
6}