Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloomz-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz-mt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz-mt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-mt") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-mt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/bloomz-mt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz-mt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloomz-mt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz-mt
- SGLang
How to use bigscience/bloomz-mt 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/bloomz-mt" \ --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/bloomz-mt", "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/bloomz-mt" \ --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/bloomz-mt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz-mt with Docker Model Runner:
docker model run hf.co/bigscience/bloomz-mt
Download pytorch_model_00014-of-00072.bin from bigscience/bloomz-mt: direct link, hf CLI and curl.
- Browser
- Download file 4.93 GB
-
https://huggingface.co/bigscience/bloomz-mt/resolve/6d77df04fc7e15a336f997f49b1a714d0bb0f106/pytorch_model_00014-of-00072.bin
- Command line
-
hf download hf://bigscience/bloomz-mt@6d77df04fc7e15a336f997f49b1a714d0bb0f106/pytorch_model_00014-of-00072.bin
-
curl -L -o pytorch_model_00014-of-00072.bin https://huggingface.co/bigscience/bloomz-mt/resolve/6d77df04fc7e15a336f997f49b1a714d0bb0f106/pytorch_model_00014-of-00072.bin
4.93 GB
- Xet hash:
- 78594403306d9cb9143f119d3e6dc2ad7829f644e3524087ab300073c9ebabc3
- Size of remote file:
- 4.93 GB
- SHA256:
- 9d55aae2ed5666382aea4dc3c9ea875de8b4a482d7ac11d8dd8dd9a593d6e663
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.