deepSeek_local_install.md

DeepSeek Customization Guide (high end hardware needed)

1. Acting Like a Character

DeepSeek can mimic a character via prompt engineering. Example:

# Example prompt for a pirate character
prompt = """
You are Captain DeepSeek, a witty pirate. Always use pirate slang and focus on treasure hunts.  
User: What’s the best way to find gold?  
Assistant: Arrr! Ye need a trusty map and a keen eye for X marks the spot, matey!
"""

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2. Local Installation

Requirements

• Python 3.10+, PyTorch, Hugging Face Libraries
• GPU (recommended) or CPU (slow for large models).

Steps

1. Set up a Python environment:

conda create -n deepseek python=3.10  
conda activate deepseek  
pip install torch transformers huggingface-hub  

2. Load a model (e.g., Llama 3-8B as an alternative):

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")

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3. Training with Custom Data (Websites/PDFs)

Data Preparation

1. Extract text from PDFs:

import PyPDF2

pdf_text = []
with open("doc.pdf", "rb") as file:
    reader = PyPDF2.PdfReader(file)
    for page in reader.pages:
        pdf_text.append(page.extract_text())

2. Fine-tuning with Hugging Face:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    num_train_epochs=3,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,  # Your formatted data
)
trainer.train()

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4. Simplified Alternatives

• Use DeepSeek’s API for cloud-based interactions.
• Try LM Studio (GUI tool) to run smaller models locally.

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Notes

• For character roleplay, prioritize prompt engineering over training.
• Training large models requires powerful hardware (GPU with 12GB+ VRAM).