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microsoft/Phi-3-mini-4k-instruct, specialized in document redaction.microsoft/Phi-3-mini-4k-instruct{"instruction": ..., "output": ...})SFTTrainer from Hugging Face TRL✅ Final weights are full precision (fp16/fp32), ready for inference.
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model = AutoModelForCausalLM.from_pretrained("your-username/phi3-mini-instruct-redactor")
4tokenizer = AutoTokenizer.from_pretrained("your-username/phi3-mini-instruct-redactor")
5
6prompt = "Remove all personal data from the following text:\nJohn Doe lives at 123 Elm Street."
7inputs = tokenizer(prompt, return_tensors="pt")
8outputs = model.generate(**inputs, max_new_tokens=128)
9print(tokenizer.decode(outputs[0]))This model is provided "as-is" without any warranties or guarantees regarding its performance, accuracy, or reliability in detecting and redacting personally identifiable information (PII) or other sensitive data.The model may fail to identify or fully redact all forms of PII, depending on input format, context, or model limitations.Use of this model is at your own risk.The authors and maintainers of this model accept no responsibility or liability for any data leakage, compliance violations, or security breaches that may occur as a result of using this model.