Image-Text-to-Text
Transformers
English
mistral
pixtral
vlm
multimodal
orbital-mechanics
hohmann-transfer-orbits
Instructions to use Taylor658/Titan-Hohmann with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taylor658/Titan-Hohmann with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Taylor658/Titan-Hohmann")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Taylor658/Titan-Hohmann", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Taylor658/Titan-Hohmann with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Taylor658/Titan-Hohmann" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taylor658/Titan-Hohmann", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Taylor658/Titan-Hohmann
- SGLang
How to use Taylor658/Titan-Hohmann 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/Titan-Hohmann" \ --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/Titan-Hohmann", "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/Titan-Hohmann" \ --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/Titan-Hohmann", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Taylor658/Titan-Hohmann with Docker Model Runner:
docker model run hf.co/Taylor658/Titan-Hohmann
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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 Hohman transfer orbits.
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- **Computational Resources**: Requires adequate computational power for optimal performance due to model size and complexity.
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- **Model License**: Apache License 2.0
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- **Dataset License**: Apache License 2.0
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---
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### Contact Information
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- **Author**: A Taylor
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- **Email**
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- **Repository**: https://github.com/ATaylorAerospace/HohmannHET
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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 Hohman transfer orbits.
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- **Computational Resources**: Requires adequate computational power for optimal performance due to model size and complexity.
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- **Model License**: Apache License 2.0
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- **Dataset License**: Apache License 2.0
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### Next Version
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- **Future Work**: Calculations for Titan, mineral rich asteroids within the Van Allen belt, and other natural satellites beyond it, including significant inclination adjustments, will be included in version 2.0
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### Contact Information
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- **Author**: A Taylor
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- **Email**
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- **Repository**: https://github.com/ATaylorAerospace/HohmannHET
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