SURESHBEEKHANI/Mistral_7B_Summarizer_SFT_GGUF

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.

---

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.

---

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.

---

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.

---

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.

---

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.

---

Contributors

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

---

License

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

---

Notebook

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