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---
license: apache-2.0
datasets:
- Taylor658/Electrohydrodynamics
base_model:
- mistralai/Mistral-Large-Instruct-2411
language:
- en
---


# Electrohydrodynamics for Hall Effect Thrusters

## Model Card

### Model Overview

**Model Name**: `mistral-7b-hall-thruster-fluid-dynamics`  
**Model Type**: Transformer-based language model  
**Languages**: English  
**License**: Apache License 2.0

This model is based on the **Mistral-Large-Instruct-2411** foundation model and is being fine-tuned on the **Taylor658/Electrohydrodynamics** dataset. It is designed to assist with understanding electrohydrodynamics, plasma-fluid interactions, and related fluid dynamic phenomena in Hall Effect Thrusters (HETs).

---

### Model Details

- **Developers**: A Taylor
- **Model Architecture**: Transformer-based with enhancements for code generation and multimodal processing
- **Parameters**: 7 Billion
- **Native Function Calling**: Supported
- **Multimodal Capabilities**: Text-based domain discussions

---

### Intended Use

- **Primary Applications**:
  - Assist aerospace engineers and researchers in analyzing plasma and fluid flows in HET channels
  - Provide support for understanding electrohydrodynamics in propulsion systems
  - Facilitate research by offering computational assistance in modeling plasma-fluid interactions
- **Usage Scenarios**:
  - Discussing the influence of magnetic fields on electron mobility
  - Explaining ionization dynamics in the thruster discharge channel
  - Interpreting simulation data and theoretical results for efficiency and plume characteristics

---

### Training Data

- **Dataset Name**: `Taylor658/Electrohydrodynamics`
- **Description**: A dataset containing textual explanations, theoretical derivations, and computational concepts related to fluid dynamics and plasma interactions in Hall thrusters.
- **Data Modalities**:
  - **Text**: Technical documentation, research summaries, and theoretical analyses
  - **Code**: 

---

### Training Procedure

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:

1. **Domain-Specific Fine-Tuning**: Adjusting the model's parameters using the `Taylor658/fluid_dynamics_test` dataset to improve performance in electrohydrodynamics.
2. **Validation and Testing**: Ensuring the model’s outputs are accurate and reliable by comparing them against established literature and computational benchmarks.
3. **Iterative Refinement**: Continuously refining responses based on domain expert feedback and real-world problem sets.

---

### How to Use

- **Input Format**:
  - Natural language queries or prompts about electrohydrodynamics, fluid flow, or plasma phenomena in Hall Effect Thrusters.
- **Examples**:
  - "Explain how the Hall parameter affects electron mobility in a Hall Effect Thruster."
  - "What are the primary factors influencing ionization efficiency in the thruster channel?"

---

### Limitations

- **Work in Progress**: The model is currently being fine-tuned; performance may improve over time. 
- **Domain Specificity**: Optimized for Hall Effect Thruster fluid dynamics, may not generalize well outside this domain.
- **Computational Resources**: Requires adequate computational power for optimal performance due to model size.

---

### Ethical Considerations

- **Accuracy**: Intended as a research and educational aid; not a substitute for expert judgment.

---

### Acknowledgements

- **Mistral AI**: For providing the Mistral-Large-Instruct-2411 foundation model.
- **Dataset Contributors**: Taylor658
- **Open-Source Community**: Gratitude for tools and libraries that supported the fine-tuning process.

---

### License

- **Model License**: Apache License 2.0
- **Dataset License**: Apache License 2.0

### Future Work

- **Next Version**: May incorporate advanced magnetohydrodynamic modeling, improved handling of variable mass flow rates, and refined treatments of plasma-wall interactions.

---

### Contact Information

- **Author**: A Taylor
- **Email** 
- **Repository**: 

---

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