Instructions to use DIAG-PSSeng/cicero_v2-phi1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DIAG-PSSeng/cicero_v2-phi1.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DIAG-PSSeng/cicero_v2-phi1.5", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DIAG-PSSeng/cicero_v2-phi1.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DIAG-PSSeng/cicero_v2-phi1.5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DIAG-PSSeng/cicero_v2-phi1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DIAG-PSSeng/cicero_v2-phi1.5"
# 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_v2-phi1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DIAG-PSSeng/cicero_v2-phi1.5

SGLang

How to use DIAG-PSSeng/cicero_v2-phi1.5 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_v2-phi1.5" \
    --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_v2-phi1.5",
		"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_v2-phi1.5" \
        --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_v2-phi1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DIAG-PSSeng/cicero_v2-phi1.5 with Docker Model Runner:
```
docker model run hf.co/DIAG-PSSeng/cicero_v2-phi1.5
```

Model Card for Model ID

Model Details

Model Description

Cicero italian legal assistant version 2. Based upon Phi1.5, fine-tuned with 1.5GB of italian civil judgements.

Developed by: Marco Calamo, Francesca De Luzi, Mattia Macrì, Tommaso Mencattini, Massimo Mecella
Model type: Phi1.5
Language (NLP): Italian
License: Apache 2.0

Model Sources

Repository: Github
Paper: Under review

Uses

Use Cicero to autocomplete sentences in a legal italian context.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import  AutoModelForCausalLM, AutoTokenizer 
import torch

model =  AutoModelForCausalLM.from_pretrained("DIAG-PSSeng/cicero_v2-phi1.5", trust_remote_code=True ,torch_dtype=torch.float16).to("cuda")

tokenizer = AutoTokenizer.from_pretrained("DIAG-PSSeng/cicero_v2-phi1.5", trust_remote_code=True)

def generate_text(model, tokenizer, prompt, length=50, do_sample=True):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    gen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=length,max_length=length)
    generated_text = tokenizer.batch_decode(gen_tokens)
    return generated_text

print(generate_text(model=model, tokenizer=tokenizer, prompt="Con ricorso del"))

Citation

BibTeX: Under review

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

F16