Instructions to use basilepp19/bloom-1b7-it-dolly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basilepp19/bloom-1b7-it-dolly with Transformers:

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

pipe = pipeline("text-generation", model="basilepp19/bloom-1b7-it-dolly")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("basilepp19/bloom-1b7-it-dolly")
model = AutoModelForCausalLM.from_pretrained("basilepp19/bloom-1b7-it-dolly")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use basilepp19/bloom-1b7-it-dolly with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "basilepp19/bloom-1b7-it-dolly"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "basilepp19/bloom-1b7-it-dolly",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/basilepp19/bloom-1b7-it-dolly

SGLang

How to use basilepp19/bloom-1b7-it-dolly 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 "basilepp19/bloom-1b7-it-dolly" \
    --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": "basilepp19/bloom-1b7-it-dolly",
		"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 "basilepp19/bloom-1b7-it-dolly" \
        --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": "basilepp19/bloom-1b7-it-dolly",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use basilepp19/bloom-1b7-it-dolly with Docker Model Runner:
```
docker model run hf.co/basilepp19/bloom-1b7-it-dolly
```

Model Card for Model ID

This model is obtained by adapting bloom-1b7 to the Italian language. Among the languages supported by the BLOOM model, there is no Italian, making its use in that context challenging. We adapt the original BLOOM model using the MAD-X language adaptation strategy. Then, the model is fine-tuned over the Italian translation of the dolly dataset.

Model Details

Model Description

We adapt the bloom-1b7 to the Italian language using the MAD-X language adaptation strategy. To produce a valuable model, we follow the same procedure proposed in: https://arxiv.org/abs/2212.09535

We use default script parameters and select a sample of 100,000 examples in the Italian language. We decided to sample data from the Filtered Oscar Dataset for the Italian Language released by Sarti.

Then, the adopted model is fine-tuned over the Italian translation of the dolly dataset. The dolly dataset was automatically transtalted by an open-source machine translation tool: https://pypi.org/project/argostranslate/

To fine-tune the adapted model, we use the script available here: https://github.com/hyintell/BLOOM-fine-tuning/tree/main

It is important to underline that when you use the adapted LLM or one of its fine-tuned models is necessary to use the tokenizer of the adapted model. The BLOOM model adapted to the Italian language is available here: https://huggingface.co/basilepp19/bloom-1b7_it.

Developed by: Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. Department of Computer Science, University of Bari Aldo Moro, Italy
Model type: BLOOM
Language(s) (NLP): Italian
License: BigScience BLOOM RAIL 1.0

Citation

Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. On the impact of Language Adaptation for Large Language Models: A case study for the Italian language using only open resources. Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023).

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Paper for basilepp19/bloom-1b7-it-dolly

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Paper • 2212.09535 • Published Dec 19, 2022 • 1