Instructions to use sprifi/OpenElisa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sprifi/OpenElisa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sprifi/OpenElisa")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sprifi/OpenElisa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use sprifi/OpenElisa with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sprifi/OpenElisa" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sprifi/OpenElisa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sprifi/OpenElisa
- SGLang
How to use sprifi/OpenElisa 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 "sprifi/OpenElisa" \ --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": "sprifi/OpenElisa", "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 "sprifi/OpenElisa" \ --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": "sprifi/OpenElisa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sprifi/OpenElisa with Docker Model Runner:
docker model run hf.co/sprifi/OpenElisa
- Model Card for SpriFi's OpenElisa AI Model
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
Model Card for SpriFi's OpenElisa AI Model
The OpenElisa is a fine-tuned, open-sourced version of SpriFi's native Elisa AI. Learn more at https://sprifi.com/
Model Details
Model Description
- Developed by: SpriFi
- Funded by: SpriFi
- Model type: Fine-tuned open-source
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model: Elisa AI by SpriFi
Model Sources
- Repository: https://github.com/sprifi
- Paper: https://huggingface.co/papers/1910.09700
- Demo: https://huggingface.co/spaces/sprifi/Elisa-AI
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
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Factors
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Results
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Model tree for sprifi/OpenElisa
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