VISION 1

🚀 VISION-1: Content Safety Analysis Model

VISION-1 is a fine - tuned version of Llama 3.1 8B Instruct, tailored for content safety analysis and moderation. It can identify and analyze potential safety risks in text, such as scams, fraud, harmful content, and inappropriate material.

🚀 Quick Start

To start using the VISION - 1 model, you can follow the code example below:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("OverseerAI/VISION-1")

# Format prompt
prompt = "Analyze the following content for safety concerns: 'Click here to win a free iPhone! Just enter your credit card details.'"
formatted_prompt = f"<s>[INST] {prompt} [/INST]"

# Generate response
inputs = tokenizer(formatted_prompt, return_tensors="pt", padding=True)
outputs = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

✨ Features

• Specialized for content safety analysis and moderation.
• Capable of identifying various types of safety concerns in text.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("OverseerAI/VISION-1")

# Format prompt
prompt = "Analyze the following content for safety concerns: 'Click here to win a free iPhone! Just enter your credit card details.'"
formatted_prompt = f"<s>[INST] {prompt} [/INST]"

# Generate response
inputs = tokenizer(formatted_prompt, return_tensors="pt", padding=True)
outputs = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

📚 Documentation

Model Details

Training Details

• Training Type: Fine - tuning
• Framework: PyTorch with DeepSpeed
• Training Data: Specialized dataset focused on content safety
• Hardware: 2x NVIDIA H100 SXM GPUs
• Training Time: ~4 epochs

Intended Use

• Content moderation
• Safety analysis
• Fraud detection
• Harmful content identification

Limitations

• Model outputs should be used as suggestions, not definitive judgments.
• May have biases from training data.
• Should be used as part of a broader content moderation strategy.
• Performance may vary based on content type and context.

Ethical Considerations

• Model should be used responsibly for content moderation.
• Human oversight recommended for critical decisions.
• Consider privacy implications when analyzing user content.
• Regular evaluation of model outputs for bias.

📄 License

The license of this model is the same as the base model, which is llama3.1.