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README.md

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-## Model Summary
+## Phi-3-Mini-128K-Instruct
 
-The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets.
-This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties.
-The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support.
+### Model Information
+Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, instruction-tuned model from Microsoft, belonging to the Phi-3 family. It has been optimized for long-context comprehension and efficient handling of complex, reasoning-dense tasks. The model supports a context length of up to 128K tokens, making it particularly suitable for scenarios involving extended conversations or long-form content generation.
 
-After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures.
-When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters.
-Resources and Technical Documentation:
+- **Name**: Phi-3-Mini-128K-Instruct
+- **Parameter Size**: 3.8 billion
+- **Model Family**: Phi-3
+- **Architecture**: Transformer with an enhanced focus on efficient context handling.
+- **Purpose**: Multilingual dialogue generation, text generation, code completion, and summarization.
+- **Training Data**: A combination of synthetic data and filtered, publicly available website data, with an emphasis on reasoning-dense properties.
+- **Supported Languages**: English (primary language).
+- **Release Date**: September 18, 2024
+- **Context Length**: 128K tokens (other versions include a [4K variant](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct))
+- **Knowledge Cutoff**: July 2023
 
-+ [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april)
-+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+### Quantized Model Files
+Phi-3 is available in several formats, catering to different computational needs and resource constraints:
 
-## Quantized Model Files
+- **ggml-model-q8_0.gguf**: 8-bit quantization, providing robust performance with a file size of 3.8 GB, suitable for resource-constrained environments.
+- **ggml-model-f16.gguf**: 16-bit floating-point format, offering enhanced precision at a larger file size of 7.2 GB.
+  
+These formats ensure that the Phi-3 Mini-128K can be adapted to a variety of systems, from low-power devices to high-end servers, making it a versatile option for deployments.
 
-Phi-3 is available in several formats, catering to different computational needs:
+### Core Library
+Phi-3-Mini-128K-Instruct can be deployed using `llama.cpp` or `transformers`, with support for high-efficiency long-context inference.
 
-- **ggml-model-q8_0.gguf**: 8-bit quantization, providing robust performance with a file size of 3.8 GB.
-- **ggml-model-f16.gguf**: Standard 16-bit floating-point format, with a larger file size of 7.2 GB for enhanced precision.
+- **Primary Framework**: `llama.cpp`  
+- **Alternate Frameworks**:
+  - `transformers` for integrations into the Hugging Face ecosystem.
+  - `vLLM` for efficient inference with optimized memory usage.
 
-These formats, ranging from 4-bit to 16-bit, accommodate various computational environments, from resource-constrained devices to high-end server
+**Library and Model Links**:
+- **Model Base**: [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
+- **Resources and Technical Documentation**:
+  - [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april)
+  - [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+
+## Safety and Responsible Use
+
+The Phi-3-Mini-128K-Instruct is part of the Phi model family, known for its rigorous dataset curation focused on educational and non-toxic sources. Due to its careful design, the Phi-3 series generally avoids generating harmful or biased outputs. This makes it a reliable choice for safety-critical applications and environments where ethical standards are paramount.
+
+### Training Philosophy
+The Phi-3 series models are intentionally trained on textbooks, research papers, and high-quality language corpora, avoiding sources that might introduce harmful, biased, or inappropriate content. As a result, Phi-3 maintains a strong adherence to safe and controlled responses, even when handling sensitive topics or instructions.
+
+### Risk Profile and Use Recommendations
+While no AI model is entirely risk-free, Phi-3's safety features minimize the likelihood of producing unwanted or offensive outputs. However, it is still recommended that users conduct scenario-specific testing to verify its behavior in deployment environments. For additional confidence, consider the following guidelines:
+
+- **Intended Use**: Education, research, and general-purpose dialogue systems.
+- **Deployment**: Suitable for low-risk applications where adherence to ethical and safety guidelines is crucial.
+- **Community Testing and Feedback**: Open to user feedback to improve safety benchmarks further and align with best practices.
+
+For more information on Phi's safety approach, refer to [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).