putin-bot-lora

Putin Bot - LoRA Adapter

LoRA fine-tuning weights for Llama 3.3 70B Instruct, trained to simulate Vladimir Putin's communication style for strategy simulation games.

Model Details

• Base Model: meta-llama/Llama-3.3-70B-Instruct
• Training Method: LoRA (Low-Rank Adaptation)
• Adapter Size: 32
• Training Data: 3,696 samples from press conferences and interviews (2000-2024)
• Training Platform: Google Cloud Vertex AI
• Training Cost: ~$40-120
• Adapter Size: 1.5GB

Training Data Sources

• Kremlin press conference transcripts (575 documents)
• Tucker Carlson interview (Feb 2024)
• Oliver Stone interviews (2017)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = "meta-llama/Llama-3.3-70B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "kennethpayne01/putin-bot-lora")

# Generate
messages = [
    {"role": "system", "content": "You are Vladimir Putin, President of Russia."},
    {"role": "user", "content": "What is your view on NATO expansion?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

Hardware Requirements

• Full precision: ~140GB VRAM (2-4x A100 80GB)
• 4-bit quantization: ~40GB VRAM (1x A100 80GB)
• 8-bit quantization: ~70GB VRAM (1x A100 80GB)

Training Configuration

• Learning rate: 0.0001
• Epochs: 3
• LoRA rank (r): 32
• LoRA alpha: 64
• Target modules: All attention layers
• Training time: ~2-4 hours
• Platform: Vertex AI with A100 GPUs

Limitations

• Model trained on public statements; may not reflect private views
• Data range 2000-2024; current events after Dec 2024 not included
• English translations may lose nuance from original Russian
• Designed for simulation/entertainment, not policy analysis

Ethical Considerations

This model is created for strategy simulation games and educational purposes. It should not be used to:

• Spread misinformation or propaganda
• Impersonate real individuals for deception
• Generate harmful or misleading content

License

This adapter is released under the Llama 3.3 license. See base model license for details.

Citation

@misc{putin-bot-lora,
  author = {Your Name},
  title = {Putin Bot LoRA Adapter},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/kennethpayne01/putin-bot-lora}}
}

Repository

Full code and training pipeline: GitHub Repository