Instructions to use bigscience/bloom-1b7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom-1b7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom-1b7")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b7") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom-1b7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom-1b7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom-1b7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom-1b7
- SGLang
How to use bigscience/bloom-1b7 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 "bigscience/bloom-1b7" \ --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": "bigscience/bloom-1b7", "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 "bigscience/bloom-1b7" \ --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": "bigscience/bloom-1b7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom-1b7 with Docker Model Runner:
docker model run hf.co/bigscience/bloom-1b7
Unable to decode text I get this error.
AttributeError: 'BloomTokenizerFast' object has no attribute 'tokenizer'
My code is something like this:
from transformers import AutoTokenizer, BloomForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b3")
model = BloomForCausalLM.from_pretrained("bigscience/bloom-1b3")
prompt = "Today I believe we can finally"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generate up to 30 tokens
outputs = model.generate(input_ids, do_sample=False, max_length=30)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
Hi @aryan1107 !
Thanks for your message,
I just tried this script:
from transformers import AutoTokenizer, BloomForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b3")
model = BloomForCausalLM.from_pretrained("bigscience/bloom-1b3")
prompt = "Today I believe we can finally"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids, do_sample=False, max_length=30)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
and seems to work fine on my side, what version of transformers are you using?
Thank you it's working I had made a typo. my bad