Instructions to use research-backup/t5-base-tweetqa-qag-np with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/t5-base-tweetqa-qag-np with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="research-backup/t5-base-tweetqa-qag-np")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("research-backup/t5-base-tweetqa-qag-np")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/t5-base-tweetqa-qag-np")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use research-backup/t5-base-tweetqa-qag-np with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "research-backup/t5-base-tweetqa-qag-np"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "research-backup/t5-base-tweetqa-qag-np",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/research-backup/t5-base-tweetqa-qag-np

SGLang

How to use research-backup/t5-base-tweetqa-qag-np 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 "research-backup/t5-base-tweetqa-qag-np" \
    --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": "research-backup/t5-base-tweetqa-qag-np",
		"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 "research-backup/t5-base-tweetqa-qag-np" \
        --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": "research-backup/t5-base-tweetqa-qag-np",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/t5-base-tweetqa-qag-np with Docker Model Runner:
```
docker model run hf.co/research-backup/t5-base-tweetqa-qag-np
```

asahi417 commited on Nov 17, 2022

Commit

29d51c9

1 Parent(s): 6a0efbd

add tokenizer

Browse files

Files changed (5) hide show

added_tokens.json +1 -0
special_tokens_map.json +1 -0
spiece.model +3 -0
tokenizer.json +0 -0
tokenizer_config.json +1 -0

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1	+ {"<hl>": 32100}

special_tokens_map.json ADDED Viewed

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1	+ {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<hl>"]}

spiece.model ADDED Viewed

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+oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
+size 791656

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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