How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MohamedIFQ/sysmlAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MohamedIFQ/sysmlAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/MohamedIFQ/sysmlAI
Quick Links

SysML AI: PlantUML Code Generator

This model is a fine-tuned version of [Base Model Name] (e.g., GPT-2, CodeGen, etc.) that generates PlantUML code from natural language descriptions. It can be used to create sequence diagrams, class diagrams, and other PlantUML diagrams, making it a valuable tool for software engineers, system architects, and anyone who needs to visualize system designs.

Model Description

  • Architecture: [Describe the base model architecture, e.g., Transformer with X layers, Y attention heads]
  • Fine-tuning Dataset: [Specify the dataset used for fine-tuning, including the number of examples, source, and data format]
  • Training Objective: [Describe the training objective, e.g., minimizing cross-entropy loss between predicted and actual PlantUML tokens]
  • Evaluation Metrics: [List the metrics used to evaluate the model, e.g., BLEU score, ROUGE score, or other relevant code generation metrics]

Intended Uses & Limitations

  • Intended Use: Generating PlantUML code from natural language descriptions to aid in system design and visualization.
  • Limitations:
    • May not handle complex or ambiguous descriptions accurately.
    • May require some manual editing of the generated code for optimal results.
    • Performance may vary depending on the complexity of the desired diagram.

How to Use

Installation:

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