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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datasets:
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- Taylor658/Electrohydrodynamics
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base_model:
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- mistralai/
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language:
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tags:
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### Model Overview
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**Model Name**: `mistral-
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**Model Type**: Transformer based language model
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**Language**: English
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**License**: MIT
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This model is based on the **Mistral-
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---
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### Acknowledgements
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- **Mistral AI**: For providing the Mistral-
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- **Dataset Contributors**: A Taylor
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datasets:
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- Taylor658/Electrohydrodynamics
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base_model:
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- mistralai/8x7B
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language:
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- en
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tags:
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### Model Overview
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**Model Name**: `mistral-8x7B-hall-thruster-fluid-dynamics`
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**Model Type**: Transformer based language model
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**Language**: English
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**License**: MIT
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This model is based on the **Mistral-8x7B** 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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### Acknowledgements
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- **Mistral AI**: For providing the Mistral-8x7B foundation model.
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- **Dataset Contributors**: A Taylor
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