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README.md

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-license: llama3.1
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+# VISION-1: Content Safety Analysis Model
+
+VISION-1 is a fine-tuned version of Llama 3.1 8B Instruct, specialized for content safety analysis and moderation. The model is trained to identify and analyze potential safety concerns in text content, including scams, fraud, harmful content, and inappropriate material.
+
+## Model Details
+
+- **Base Model**: Llama 3.1 8B Instruct
+- **Training Data**: Specialized safety and content moderation dataset
+- **Model Type**: Decoder-only transformer
+- **Parameters**: 8 billion
+- **Training Infrastructure**: 2x NVIDIA H100 SXM GPUs
+- **License**: Same as base model
+
+## Usage
+
+```python
+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)
+```
+
+## 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