This model is trained to perform text anonymization with proper tool calling output format. It can identify and replace personally identifiable information (PII) while maintaining semantic meaning and context.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained("eternis/eternis_sft_tool_calling_Qwen4B_26jul_merged", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("eternis/eternis_sft_tool_calling_Qwen4B_26jul_merged", trust_remote_code=True)

# Example usage
input_text = "John Doe works at Google in New York"
# ... generate anonymized output with tool calls

Training

This model was trained using a multi-adapter approach:

Base Qwen3-4B model
Adapter A: Specialized in anonymization tasks
Adapter B: Specialized in tool calling format

License

MIT License

eternisai/anonymizer-4B-SFTonly

Qwen3-4B Anonymizer Tool Call Merged Model

Model Description

Usage

Training

License

eternisai/
anonymizer-4B-SFTonly