--- license: mit language: - en tags: - gguf - phi-3 - llama.cpp - quantized - redactor - q4_k_m - text-generation pipeline_tag: text-generation datasets: - King-Harry/NinjaMasker-PII-Redaction-Dataset base_model: - microsoft/Phi-3-mini-4k-instruct - christopherheuer/phi3-mini-4k-instruct-pii-redactor --- # ⚡ Phi-3 Mini Instruct Redactor - GGUF (Q4_K_M) This is a **GGUF-format**, Q4_K_M **quantized version** of [`phi3-mini-4k-instruct-pii-redactor`](https://huggingface.co/christopherheuer/phi3-mini-4k-instruct-pii-redactor), optimized for **local inference with llama.cpp** and other GGUF-compatible tools. The model was LoRA-finetuned for redaction/instruction tasks and fully merged before conversion and quantization. ## 🛠 Format & Details - **Model Base:** `microsoft/Phi-3-mini-4k-instruct` - **LoRA Merged:** ✅ - **GGUF Format:** ✅ - **Quantization:** Q4_K_M (4-bit grouped, high quality) - **File:** `phi3-mini-instruct-redactor-q4km.gguf` - **Model Size:** ~2–3 GB ## 💻 Usage with `llama.cpp` ```bash ./llama-cli.exe -m phi3-mini-instruct-redactor-q4km.gguf --prompt "John Doe, born in 1985, lives in Berlin." -n 128 -ngl 100 ``` ## ✅ Works with: - llama.cpp (CUDA build) - text-generation-webui - KoboldCpp - llamacpp-python - LM Studio ## 📖 Dataset Attribution This model was fine-tuned using a modified version of the [NinjaMasker-PII-Redaction Dataset by King-Harry](https://huggingface.co/datasets/King-Harry/NinjaMasker-PII-Redaction-Dataset), which is licensed under the Apache 2.0 License. The dataset was reformatted to suit the training requirements of this model. ## ⚠️ Disclaimer: >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. ## 📜 License MIT License