---
license: llama3.3
base_model: meta-llama/Llama-3.3-70B-Instruct
tags:
  - lora
  - llama
  - character-simulation
  - political-persona
language:
  - en
  - ru
---

# 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

```python
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

```bibtex
@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](#)