Text Generation
Transformers
PyTorch
ONNX
Safetensors
mt5
text2text-generation
Eval Results (legacy)
Instructions to use bigscience/mt0-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/mt0-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/mt0-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-base") model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/mt0-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/mt0-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/mt0-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/mt0-base
- SGLang
How to use bigscience/mt0-base 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/mt0-base" \ --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/mt0-base", "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/mt0-base" \ --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/mt0-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/mt0-base with Docker Model Runner:
docker model run hf.co/bigscience/mt0-base
Commit History
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Adding ONNX file of this model (#5)
- Adding ONNX file of this model (0ca0de56e99d29de28b5505044046eeaddb8ac0a) Co-authored-by: i <Gidz@users.noreply.huggingface.co>0c50004
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Update README.md
e7a13e3
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Adding `safetensors` variant of this model (#4)
- Adding `safetensors` variant of this model (adb3cf68c40228d0441c941257ed26f99f0034ce) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>0a60249
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Update README.md (#1)
- Update README.md (d8040f895b7124bcfb7862ed5a7d19412960def7)00b95dc
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Correct mt5-small & mt5-base positions
7cc5660
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Update README.md
e91efc2
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Update steps
5e44aab -
Create README.md
a04867a
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Add checkpoint_1025000
thomasw215031992
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initial commit
Thomas Wang93f36d6