Instructions to use DIAG-PSSeng/cicero-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DIAG-PSSeng/cicero-gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DIAG-PSSeng/cicero-gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DIAG-PSSeng/cicero-gpt2") model = AutoModelForCausalLM.from_pretrained("DIAG-PSSeng/cicero-gpt2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DIAG-PSSeng/cicero-gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DIAG-PSSeng/cicero-gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DIAG-PSSeng/cicero-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DIAG-PSSeng/cicero-gpt2
- SGLang
How to use DIAG-PSSeng/cicero-gpt2 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 "DIAG-PSSeng/cicero-gpt2" \ --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": "DIAG-PSSeng/cicero-gpt2", "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 "DIAG-PSSeng/cicero-gpt2" \ --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": "DIAG-PSSeng/cicero-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DIAG-PSSeng/cicero-gpt2 with Docker Model Runner:
docker model run hf.co/DIAG-PSSeng/cicero-gpt2
cicero-gpt2
GroNLP/gpt2-small-italian version fine-tuned with italian civil judgments.
Model Details
Model Description
- Developed by: Marco Calamo, Francesca De Luzi, Mattia Macrì, Tommaso Mencattini, Massimo Mecella
- Model type: gpt2-small-italian
- Language(s) (NLP): italian
- License: openrail
- Finetuned from model: GroNLP/gpt-2-small
Model Sources
- Repository: Github
- Paper [optional]: [More Information Needed]
Uses
Direct Use
Used to generate part of sentences based upon user input. All sensible data are hidden by design.
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
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
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Model Architecture and Objective
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Compute Infrastructure
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Software
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