Instructions to use swap-uniba/bloom-1b7-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swap-uniba/bloom-1b7-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swap-uniba/bloom-1b7-it")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("swap-uniba/bloom-1b7-it") model = AutoModelForCausalLM.from_pretrained("swap-uniba/bloom-1b7-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use swap-uniba/bloom-1b7-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "swap-uniba/bloom-1b7-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swap-uniba/bloom-1b7-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/swap-uniba/bloom-1b7-it
- SGLang
How to use swap-uniba/bloom-1b7-it 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 "swap-uniba/bloom-1b7-it" \ --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": "swap-uniba/bloom-1b7-it", "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 "swap-uniba/bloom-1b7-it" \ --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": "swap-uniba/bloom-1b7-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use swap-uniba/bloom-1b7-it with Docker Model Runner:
docker model run hf.co/swap-uniba/bloom-1b7-it
Model Card for Model ID
The model is obtained by performing language adaptation on the original bloom-1b7 model. In detail, we continued the pre-training on Italian-specific data without adaptation of the vocabulary. We use about 2.8M documents obtained from Italian Wikimedia dumps (swap-uniba/itwiki-march-2024). The model is trained for one epoch using LoRA and SFT.
Model Details
Model Description
- Developed by: SWAP Research Group, Department of Computer Science, University of Bari Aldo Moro
- Model type: BLOOM
- Language(s) (NLP): Italian
- License: bigscience-bloom-rail-1.0
- Finetuned from model [optional]: bloom-1b7
Training Details
Training Data
2.8M documents obtained from Italian Wikimedia dumps (swap-uniba/itwiki-march-2024).
Training Procedure
LoRA and SFT.
Training Hyperparameters
- Training regime: fp16
Citation [optional]
BibTeX:
APA:
Model Card Authors [optional]
Pierpaolo Basile, University of Bari Aldo Moro, Italy.
Model Card Contact
Pierpaolo Basile, University of Bari Aldo Moro, Italy.
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