Trump Mistral Adapter

This is a LoRA adapter for the Mistral-7B-Instruct-v0.2 model, fine-tuned to emulate Donald Trump's distinctive speaking style, discourse patterns, and policy positions.

Model Details

Base Model: mistralai/Mistral-7B-Instruct-v0.2
Model Type: LoRA adapter (Low-Rank Adaptation)
LoRA Rank: 16
Language: English
Training Focus: Emulation of Donald Trump's communication style and response patterns

Intended Use

This model is designed for:

Educational and research purposes related to political discourse and communication styles
Interactive simulations for understanding political rhetoric
Creative applications exploring political communication

Training Data

This adapter was fine-tuned on two key datasets:

These datasets were processed into an instruction format:

Training Procedure

Framework: Hugging Face Transformers and PEFT
Optimization: 4-bit quantization for memory efficiency
LoRA Configuration:
- r=16
- lora_alpha=64
- lora_dropout=0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Parameters:
- Batch size: 4
- Gradient accumulation steps: 4
- Learning rate: 2e-4
- Epochs: 3
- Learning rate scheduler: cosine
- Optimizer: paged_adamw_8bit
- BF16 precision

Limitations and Biases

The model is designed to mimic a speaking style and may not always provide factually accurate information
While it emulates Trump's rhetoric, it does not represent his actual views or statements
The model may reproduce biases present in the training data
Not suitable for production applications requiring factual accuracy without RAG enhancement

Usage

This adapter should be applied to the Mistral-7B-Instruct-v0.2 base model:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

# Load base model with 4-bit quantization
base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# Apply the adapter
model = PeftModel.from_pretrained(model, "nnat03/trump-mistral-adapter")

# Generate a response
prompt = "What's your plan for border security?"
input_text = f"<s>[INST] {prompt} [/INST]"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("[/INST]")[-1].strip())

Citation and Acknowledgments

If you use this model in your research, please cite: @misc{nnat03-trump-mistral-adapter, author = {nnat03}, title = {Trump Mistral Adapter}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/nnat03/trump-mistral-adapter}} }

Ethical Considerations

This model is created for educational and research purposes. It attempts to mimic the speaking style of a public figure but does not represent their actual views or statements. Use responsibly.

nnat03
/

trump-mistral-adapter