metadata
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
- ar
datasets:
- Abdulrhman37/metallurgy-qa
pipeline_tag: text2text-generation
Fine-Tuned Llama Model for Metallurgy and Materials Science
- Developed by: Abdulrhman37
- License: Apache-2.0
- Base Model: unsloth/meta-llama-3.1-8b-bnb-4bit
This fine-tuned Llama model specializes in metallurgy, materials science, and engineering. It has been enhanced to provide precise and detailed responses to technical queries, making it a valuable tool for professionals, researchers, and enthusiasts in the field.
🛠️ Training Details
This model was fine-tuned with:
- Unsloth: Enabled 2x faster training using efficient parameter optimization.
- Hugging Face TRL: Used for advanced fine-tuning and training capabilities.
Fine-tuning focused on enhancing domain-specific knowledge using a dataset curated from various metallurgical research and practical case studies.
🔑 Features
- Supports text generation with scientific and technical insights.
- Provides domain-specific reasoning with references to key metallurgical principles and mechanisms.
- Built for fast inference with bnb-4bit quantization for optimized performance.
🌟 Example Use Cases
- Material property analysis (e.g., "How does adding rare earth elements affect magnesium alloys?").
- Failure mechanism exploration (e.g., "What causes porosity in gas metal arc welding?").
- Corrosion prevention methods (e.g., "How does cathodic protection work in marine environments?").
📦 How to Use
follow this notebook for help to use the model
📧 Contact
For any inquiries, feedback, or collaboration opportunities, feel free to reach out:
- Email: abdodebo3@gmail.com
- GitHub
- Phone: +20 1026821545
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
