Text Generation
Transformers
English
electrohydrodynamics
hall-effect-thrusters
plasma-physics
computational-fluid-dynamics
text-generation-inference
mistral-large
128k-context
multimodal
Instructions to use Taylor658/Electrohydrodynamics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taylor658/Electrohydrodynamics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Taylor658/Electrohydrodynamics")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Taylor658/Electrohydrodynamics", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Taylor658/Electrohydrodynamics with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Taylor658/Electrohydrodynamics" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taylor658/Electrohydrodynamics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Taylor658/Electrohydrodynamics
- SGLang
How to use Taylor658/Electrohydrodynamics with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Taylor658/Electrohydrodynamics" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taylor658/Electrohydrodynamics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Taylor658/Electrohydrodynamics" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taylor658/Electrohydrodynamics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Taylor658/Electrohydrodynamics with Docker Model Runner:
docker model run hf.co/Taylor658/Electrohydrodynamics
Update README.md
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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/Electrohydrodynamics** dataset. It is designed to assist with understanding electrohydrodynamics, plasma
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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
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- **Usage Scenarios**:
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- Discussing the influence of magnetic fields on electron mobility
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### Limitations
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- **Work in Progress**: The model is currently being fine
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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
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- **Mistral AI**: For providing the Mistral-Large-Instruct-2411 foundation model.
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- **Dataset Contributors**: A Taylor
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- **Open-Source Community**: Gratitude for tools and libraries that supported the fine-tuning process.
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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
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---
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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/Electrohydrodynamics** 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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---
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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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| 47 |
- 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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-
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- **Usage Scenarios**:
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- Discussing the influence of magnetic fields on electron mobility
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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 gpu for optimal performance due to model size.
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---
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- **Mistral AI**: For providing the Mistral-Large-Instruct-2411 foundation model.
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- **Dataset Contributors**: A Taylor
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---
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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 refinement of plasma wall interactions.
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