Text Generation
Transformers
PyTorch
ONNX
Safetensors
mt5
text2text-generation
Eval Results (legacy)
Instructions to use bigscience/mt0-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/mt0-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/mt0-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large") model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/mt0-large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/mt0-large" # 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-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/mt0-large
- SGLang
How to use bigscience/mt0-large 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-large" \ --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-large", "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-large" \ --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-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/mt0-large with Docker Model Runner:
docker model run hf.co/bigscience/mt0-large
Commit History
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Adding ONNX file of this model
Beep boop I am the [ONNX export bot 🤖🏎️](https://huggingface.co/spaces/onnx/export). On behalf of [Gidz](https://huggingface.co/Gidz), I would like to add to this repository the model converted to ONNX. What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at [onnx.ai](https://onnx.ai/)! The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with 🤗 Optimum through ONNX Runtime, check out how [here](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models)!9675515
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Update README.md
edda1ff
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Fix bad link to xP3 dataset (#3)
- Fix bad link to xP3 dataset (08101e41bf0ab2d21f37c194263be34320660eef) Co-authored-by: Nicolas Brousse <orieg@users.noreply.huggingface.co>0bc855a
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Adding `safetensors` variant of this model (#4)
- Adding `safetensors` variant of this model (839356acb1a255240c494fa1d1220eb857d9a7df) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>d0e648d
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Update README.md (#2)
- Update README.md (fe06671b7a9561fc6c10ad30242ea69b59be21e7)14122f9
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corrections to the README.md (#1)
- corrections to the README.md (3dcfa38f346af1fb3f81b269b23d9618e5c9ee73) Co-authored-by: Sourab Mangrulkar <smangrul@users.noreply.huggingface.co>7e9d6ef
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Update README.md
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Update steps
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Update TPU
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Add results
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Add checkpoint_1025000
thomasw2142d16c3
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initial commit
Thomas Wang92097b5