Text Generation
Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
text-generation-inference
Instructions to use google/t5-small-ssm-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-small-ssm-nq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="google/t5-small-ssm-nq")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-small-ssm-nq") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-small-ssm-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use google/t5-small-ssm-nq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "google/t5-small-ssm-nq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/t5-small-ssm-nq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/google/t5-small-ssm-nq
- SGLang
How to use google/t5-small-ssm-nq 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 "google/t5-small-ssm-nq" \ --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": "google/t5-small-ssm-nq", "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 "google/t5-small-ssm-nq" \ --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": "google/t5-small-ssm-nq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use google/t5-small-ssm-nq with Docker Model Runner:
docker model run hf.co/google/t5-small-ssm-nq
Commit ·
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Parent(s): b3cd6dc
Update README.md
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README.md
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@@ -41,8 +41,8 @@ The model can be used as follows for **closed book question answering**:
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```python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-
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t5_tok = AutoTokenizer.from_pretrained("google/t5-
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input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
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gen_output = t5_qa_model.generate(input_ids)[0]
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```python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-small-ssm-nq")
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t5_tok = AutoTokenizer.from_pretrained("google/t5-small-ssm-nq")
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input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
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gen_output = t5_qa_model.generate(input_ids)[0]
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