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license: apache-2.0 |
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datasets: |
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- Taylor658/fluid_dynamics_test |
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base_model: |
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- mistralai/Mistral-Large-Instruct-2411 |
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language: |
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- en |
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# Electrohydrodynamics for Hall Effect Thrusters |
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## Model Card |
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### Model Overview |
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**Model Name**: `mistral-8b-hall-thruster-fluid-dynamics` |
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**Model Type**: Transformer-based language model |
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**Languages**: English |
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**License**: Apache License 2.0 |
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This model is based on the **Mistral-Large-Instruct-2411** foundation model and is being fine-tuned on the **Taylor658/fluid_dynamics_test** dataset. It is designed to assist with understanding electrohydrodynamics, plasma-fluid interactions, and related fluid dynamic phenomena in Hall Effect Thrusters (HETs). |
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### Model Details |
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- **Developers**: A Taylor |
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- **Model Architecture**: Transformer-based with enhancements for code generation and multimodal processing |
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- **Parameters**: 7 Billion |
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- **Native Function Calling**: Supported |
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- **Multimodal Capabilities**: Text-based domain discussions |
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### Intended Use |
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- **Primary Applications**: |
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- Assist aerospace engineers and researchers in analyzing plasma and fluid flows in HET channels |
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- Provide support for understanding electrohydrodynamics in propulsion systems |
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- Facilitate research by offering computational assistance in modeling plasma-fluid interactions |
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- **Usage Scenarios**: |
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- Discussing the influence of magnetic fields on electron mobility |
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- Explaining ionization dynamics in the thruster discharge channel |
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- Interpreting simulation data and theoretical results for efficiency and plume characteristics |
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### Training Data |
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- **Dataset Name**: `Taylor658/fluid_dynamics_test` |
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- **Description**: A dataset containing textual explanations, theoretical derivations, and computational concepts related to fluid dynamics and plasma interactions in Hall thrusters. |
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- **Data Modalities**: |
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- **Text**: Technical documentation, research summaries, and theoretical analyses |
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- **Code**: |
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### Training Procedure |
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The model will be fine tuned to enhance its capabilities in handling advanced fluid dynamics and plasma physics scenarios relevant to Hall Effect Thrusters. Key enhancements include: |
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1. **Domain-Specific Fine-Tuning**: Adjusting the model's parameters using the `Taylor658/fluid_dynamics_test` dataset to improve performance in electrohydrodynamics. |
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2. **Validation and Testing**: Ensuring the model’s outputs are accurate and reliable by comparing them against established literature and computational benchmarks. |
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3. **Iterative Refinement**: Continuously refining responses based on domain expert feedback and real-world problem sets. |
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### How to Use |
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- **Input Format**: |
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- Natural language queries or prompts about electrohydrodynamics, fluid flow, or plasma phenomena in Hall Effect Thrusters. |
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- **Examples**: |
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- "Explain how the Hall parameter affects electron mobility in a Hall Effect Thruster." |
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- "What are the primary factors influencing ionization efficiency in the thruster channel?" |
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### Limitations |
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- **Work in Progress**: The model is currently being fine-tuned; performance may improve over time. |
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- **Domain Specificity**: Optimized for Hall Effect Thruster fluid dynamics, may not generalize well outside this domain. |
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- **Computational Resources**: Requires adequate computational power for optimal performance due to model size. |
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### Ethical Considerations |
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- **Accuracy**: Intended as a research and educational aid; not a substitute for expert judgment. |
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### Acknowledgements |
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- **Mistral AI**: For providing the Mistral-Large-Instruct-2411 foundation model. |
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- **Dataset Contributors**: Taylor658 |
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- **Open-Source Community**: Gratitude for tools and libraries that supported the fine-tuning process. |
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### License |
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- **Model License**: Apache License 2.0 |
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- **Dataset License**: Apache License 2.0 |
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### Future Work |
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- **Next Version**: May incorporate advanced magnetohydrodynamic modeling, improved handling of variable mass flow rates, and refined treatments of plasma-wall interactions. |
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### Contact Information |
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- **Author**: A Taylor |
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- **Email** |
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- **Repository**: |
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