Text Generation
Transformers
PyTorch
English
t5
text2text-generation
SQL
plSQL
english
text-generation-inference
Instructions to use MRNH/flan-t5-large-PLsql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRNH/flan-t5-large-PLsql with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MRNH/flan-t5-large-PLsql")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MRNH/flan-t5-large-PLsql") model = AutoModelForSeq2SeqLM.from_pretrained("MRNH/flan-t5-large-PLsql") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MRNH/flan-t5-large-PLsql with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MRNH/flan-t5-large-PLsql" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MRNH/flan-t5-large-PLsql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MRNH/flan-t5-large-PLsql
- SGLang
How to use MRNH/flan-t5-large-PLsql 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 "MRNH/flan-t5-large-PLsql" \ --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": "MRNH/flan-t5-large-PLsql", "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 "MRNH/flan-t5-large-PLsql" \ --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": "MRNH/flan-t5-large-PLsql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MRNH/flan-t5-large-PLsql with Docker Model Runner:
docker model run hf.co/MRNH/flan-t5-large-PLsql
Upload tokenizer
Browse files- tokenizer_config.json +7 -0
tokenizer_config.json
CHANGED
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@@ -104,9 +104,16 @@
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 100,
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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"tokenizer_class": "T5Tokenizer",
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"unk_token": "<unk>"
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}
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 100,
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"max_length": 480,
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"model_max_length": 512,
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"pad_to_multiple_of": null,
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"pad_token": "<pad>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "T5Tokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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}
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