Mistral 7B Summarizer SFT GGUF

🚀 Mistral 7B Text Summarizer

The Mistral 7B Text Summarizer is a powerful tool leveraging the Mistral 7B architecture for efficient text summarization. It uses LoRA techniques to enhance performance and is fine - tuned on specific datasets for robust results.

🚀 Quick Start

The Mistral 7B Text Summarizer is a remarkable model crafted for text summarization. It harnesses the Mistral 7B architecture and applies Low - Rank Adaptation (LoRA) techniques to improve fine - tuning efficiency and optimize performance.

✨ Features

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

📚 Documentation

Overview

• Model Name: Mistral 7B Text Summarizer
• Model ID: SURESHBEEKHANI/Mistral_7B_Summarizer_SFT_GGUF
• Framework: Hugging Face Transformers

Task

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

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 effective handling of longer sequences.

Use Cases

Applications

Designed for tasks that need concise summaries of long texts, documents, or articles.

Scenarios

Ideal for domains such as content generation, report summarization, and information distillation.

Deployment

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

Limitations

• Context Length: Although optimized for extended sequences, extremely long documents may demand additional memory and computational power.
• Specialized Domains: Performance might be inconsistent in niche areas underrepresented in the training dataset.

Ethical Considerations

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

Contributors

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

Notebook

Access the implementation notebook for this model here. This notebook offers detailed steps for fine - tuning and deploying the model.

📄 License

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