Text Generation
Transformers
Safetensors
llama
code-generation
plantuml
text-to-code
text-generation-inference
Instructions to use MohamedIFQ/sysmlAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohamedIFQ/sysmlAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MohamedIFQ/sysmlAI")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MohamedIFQ/sysmlAI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MohamedIFQ/sysmlAI with 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
- SGLang
How to use MohamedIFQ/sysmlAI 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 "MohamedIFQ/sysmlAI" \ --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": "MohamedIFQ/sysmlAI", "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 "MohamedIFQ/sysmlAI" \ --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": "MohamedIFQ/sysmlAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MohamedIFQ/sysmlAI with Docker Model Runner:
docker model run hf.co/MohamedIFQ/sysmlAI
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license: mit
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- text-generation
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This is a fine-tuned model for text generation.
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- code-generation
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- plantuml
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- text-to-code
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- text-generation
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library_name: transformers
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# SysML AI: PlantUML Code Generator
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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.
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## Model Description
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- **Architecture:** [**Describe the base model architecture, e.g., Transformer with X layers, Y attention heads**]
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- **Fine-tuning Dataset:** [**Specify the dataset used for fine-tuning, including the number of examples, source, and data format**]
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- **Training Objective:** [**Describe the training objective, e.g., minimizing cross-entropy loss between predicted and actual PlantUML tokens**]
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- **Evaluation Metrics:** [**List the metrics used to evaluate the model, e.g., BLEU score, ROUGE score, or other relevant code generation metrics**]
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## Intended Uses & Limitations
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- **Intended Use:** Generating PlantUML code from natural language descriptions to aid in system design and visualization.
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- **Limitations:**
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- May not handle complex or ambiguous descriptions accurately.
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- May require some manual editing of the generated code for optimal results.
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- Performance may vary depending on the complexity of the desired diagram.
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## How to Use
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**Installation:**
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