Create README.md

This model is an ethically fine-tuned version of Llama 2, specifically trained to detect and flag private or sensitive information within natural text. It serves as a powerful tool for data privacy and security, capable of identifying potentially vulnerable data such as:

API keys
Personally Identifiable Information (PII)
Financial data
Confidential business information
Login credentials

Key Features:

Analyzes natural language input to identify sensitive content
Provides explanations for detected sensitive information
Helps prevent accidental exposure of private data
Supports responsible data handling practices

Use Cases:

Content moderation
Data loss prevention
Compliance checks for GDPR, HIPAA, etc.
Security audits of text-based communications

This model aims to enhance data protection measures and promote ethical handling of sensitive information in various applications and industries.

README.md

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+---
+license: apache-2.0
+datasets:
+- synapsecai/synthetic-sensitive-information
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+---
+
+Model Information
+
+model_name = "NousResearch/Llama-2-7b-chat-hf"
+
+dataset_name = "synapsecai/synthetic-sensitive-information"
+
+
+QLoRA parameters
+
+lora_r = 32
+
+lora_alpha = 8
+
+lora_dropout = 0.1
+
+
+BitsAndBytes parameters
+
+use_4bit = True
+
+bnb_4bit_compute_dtype = "float16"
+
+bnb_4bit_quant_type = "nf4"
+
+use_nested_quant = False
+
+
+Training Arguments parameters
+
+num_train_epochs = 1
+
+fp16 = False
+
+bf16 = False
+
+per_device_train_batch_size = 32
+
+per_device_eval_batch_size = 8
+
+gradient_accumulation_steps = 4
+
+gradient_checkpointing = True
+
+max_grad_norm = 0.3
+
+learning_rate = 2e-4
+
+weight_decay = 0.001
+
+optim = "paged_adamw_32bit"
+
+lr_scheduler_type = "cosine"
+
+max_steps = -1
+
+warmup_ratio = 0.03
+
+group_by_length = True
+
+save_steps = 0
+
+logging_steps = 25
+
+
+SFT parameters
+
+max_seq_length = None
+
+packing = False