cahlen/cdg-trending_MickeyRourke_JoJoSiwa_100

Mickey Rourke & JoJo Siwa: The blurred lines between celebrity and reality in the age of social media - Generated by Conversation Dataset Generator

This dataset was generated using the Conversation Dataset Generator script available at https://cahlen.github.io/conversation-dataset-generator/.

Generation Parameters

• —Number of Conversations Requested: 100
• —Number of Conversations Successfully Generated: 100
• —Total Turns: 1090
• —Model ID: meta-llama/Meta-Llama-3-8B-Instruct
• —Generation Mode: Mode: Creative Brief (--creative-brief "A discussion between Mickey Rourke and JoJo Siwa about the trending controversy on Celebrity Big Brother UK following homophobic remarks and subsequent apologies.")
• —Persona 1 Search Term: Mickey Rourke Celebrity Big Brother homophobic comments
• —Persona 2 Search Term: JoJo Siwa response apology homophobic remark
• —Note: Personas were generated once from the brief. Topic/Scenario/Style were varied for each example based on this brief. Parameters below reflect the last successful example.
• —Topic: The blurred lines between celebrity and reality in the age of social media
• —Scenario: A heated debate between Mickey and JoJo at a red-carpet event, where they're both promoting their respective projects
• —Style: Passionate, yet nuanced, with a focus on exploring the complexities of fame and responsibility
• —Included Points: apology, forgiveness, accountability, understanding, empathy, controversy, LGBTQ+ community

Personas

Mickey Rourke Description: A seasoned Hollywood actor, known for his tough-guy persona and rough-around-the-edges demeanor. Often speaks in a gruff, gravelly tone, with a tendency to downplay or dismiss criticism. Uses colloquialisms and slang, but can come across as insensitive or dismissive. Has a reputation for being outspoken and unapologetic, even in the face of controversy. -> maps to role: human

JoJo Siwa Description: A young, bubbly pop star, known for her bright personality and unwavering optimism. Often speaks in a cheerful, enthusiastic tone, with a tendency to use catchphrases and soundbites. Has a strong sense of social justice and is unafraid to speak her mind, especially when it comes to issues affecting the LGBTQ+ community. Has a reputation for being kind and compassionate, with a strong connection to her fans. -> maps to role: gpt

Usage

To use this dataset:

1. Clone the repository:

git lfs install
git clone https://huggingface.co/datasets/cahlen/cdg-trending_MickeyRourke_JoJoSiwa_100

2. Load in Python:

from datasets import load_dataset

dataset = load_dataset("cahlen/cdg-trending_MickeyRourke_JoJoSiwa_100")

# Access the data (e.g., the training split)
print(dataset['train'][0])

LoRA Training Example (Basic)

Below is a basic example of how you might use this dataset to fine-tune a small model like google/gemma-2b-it using LoRA with the PEFT and TRL libraries.

Note: This requires installing additional libraries: pip install -U transformers datasets accelerate peft trl bitsandbytes torch

import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
from trl import SFTTrainer

# 1. Load the dataset
dataset_id = "cahlen/cdg-trending_MickeyRourke_JoJoSiwa_100"
dataset = load_dataset(dataset_id)

# 2. Load Base Model & Tokenizer (using a small model like Gemma 2B)
model_id = "google/gemma-2b-it"

# Quantization Config (optional, for efficiency)
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16 # or torch.float16
)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
# Set padding token if necessary (Gemma's is <pad>)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.pad_token_id = tokenizer.eos_token_id

# Model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto", # Automatically place model shards
    trust_remote_code=True
)

# Prepare model for k-bit training if using quantization
model = prepare_model_for_kbit_training(model)

# 3. LoRA Configuration
lora_config = LoraConfig(
    r=8, # Rank
    lora_alpha=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], # Adjust based on model architecture
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

# 4. Training Arguments (minimal example)
training_args = TrainingArguments(
    output_dir="./lora-adapter-Mickey Rourke-JoJo Siwa", # Choose a directory
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    num_train_epochs=1, # Use 1 epoch for a quick demo
    logging_steps=10,
    save_steps=50, # Save adapter periodically
    fp16=False, # Use bf16 if available, otherwise fp16
    bf16=torch.cuda.is_bf16_supported(),
    optim="paged_adamw_8bit", # Use paged optimizer for efficiency
    report_to="none" # Disable wandb/tensorboard for simple example
)

# 5. Create SFTTrainer
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset['train'], # Assumes 'train' split exists
    peft_config=lora_config,
    tokenizer=tokenizer,
    args=training_args,
    max_seq_length=512, # Adjust as needed
    dataset_text_field="content", # Use content field directly 
    packing=True, # Pack sequences for efficiency
)

# 6. Train
print("Starting LoRA training...")
trainer.train()

### 7. Save the LoRA adapter
# Use a fixed string for the example output directory
trainer.save_model("./lora-adapter-output-directory") 
print(f"LoRA adapter saved to ./lora-adapter-output-directory")

Dataset Format (JSON Lines source)

Each row in the dataset contains the following keys:

• —conversation_id: Unique identifier for the conversation
• —turn_number: The sequential number of the turn within a conversation
• —role: Either 'human' or 'gpt' indicating who is speaking
• —speakername: The actual name of the speaker (e.g., '{finalpersona1}' or '{final_persona2}')
• —topic: The conversation topic
• —scenario: The scenario in which the conversation takes place
• —style: The stylistic direction for the conversation
• —include_points: Specific points to include in the conversation
• —content: The actual text content of the turn