---
license: mit
datasets:
- SURESHBEEKHANI/text-Summarize
language:
- en
base_model:
- unsloth/mistral-7b-v0.3
pipeline_tag: summarization
---
# Mistral 7B Text Summarizer

## Overview
**Model Name:** Mistral 7B Text Summarizer  
**Model ID:** SURESHBEEKHANI/Mistral_7B_Summarizer_SFT_GGUF  

**Framework:** Hugging Face Transformers  

The Mistral 7B Text Summarizer is a powerful model designed for text summarization tasks. It leverages the Mistral 7B architecture and incorporates Low-Rank Adaptation (LoRA) techniques to enhance fine-tuning efficiency and optimize performance.

---

## Task
**Task:** Text Summarization  
**Domain:** General-purpose, capable of summarizing content across diverse domains.

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## Key Features

- **Architecture:** Utilizes the advanced Mistral 7B transformer-based architecture.  
- **Fine-tuning:** Implements Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters to boost performance and reduce computational costs.  
- **Inference Optimization:** Designed for fast and efficient inference using gradient checkpointing and optimized data management.  
- **Quantization:** Supports 4-bit quantization, significantly reducing memory usage and computation time while maintaining accuracy.  
- **Dataset:** Fine-tuned on the SURESHBEEKHANI text-summarizer dataset for robust performance.

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## Performance Metrics

- **Maximum Sequence Length:** Supports up to 2048 tokens.  
- **Precision:** Configurable to `float16` or `float32` for hardware optimization.  
- **Training Method:** Fine-tuned using Supervised Fine-Tuning (SFT) through the Hugging Face TRL library.  
- **Efficiency:** Optimized for reduced memory footprint, enabling larger batch sizes and handling longer sequences effectively.

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## Use Cases

### Applications
Designed for tasks requiring concise summaries of lengthy texts, documents, or articles.

### Scenarios
Ideal for domains like content generation, report summarization, and information distillation.

### Deployment
Efficient for use in production systems requiring scalable and fast text summarization.

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## Limitations

- **Context Length:** While optimized for extended sequences, extremely long documents may require additional memory and computational power.  
- **Specialized Domains:** Performance may be inconsistent in niche areas that are underrepresented in the training dataset.

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## Ethical Considerations

- **Bias Mitigation:** Steps have been taken to reduce biases inherent in the training data and to ensure fairness in generated summaries.  
- **Privacy:** The model is designed to respect user privacy by adhering to best practices in handling input text data.  
- **Transparency:** Comprehensive documentation and model cards are provided to foster trust and understanding in AI-driven summarization.

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## Contributors

- **Fine-Tuning:** Suresh Beekhani  
- **Dataset:** Developed and fine-tuned using the SURESHBEEKHANI text-summarizer dataset.

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## License

**License:** Open-source under Hugging Face and unsloth licenses, allowing free use and modification.

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## Notebook

Access the implementation notebook for this model[here](https://github.com/SURESHBEEKHANI/Advanced-LLM-Fine-Tuning/blob/main/FineTuning_Mistral_7B__SFT_Summarize_GGUF.ipynb). This notebook provides detailed steps for fine-tuning and deploying the model.