Instructions to use jmeadows17/MathT5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jmeadows17/MathT5-large with Transformers:

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

pipe = pipeline("text-generation", model="jmeadows17/MathT5-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("jmeadows17/MathT5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("jmeadows17/MathT5-large", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jmeadows17/MathT5-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jmeadows17/MathT5-large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jmeadows17/MathT5-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jmeadows17/MathT5-large

SGLang

How to use jmeadows17/MathT5-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 "jmeadows17/MathT5-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": "jmeadows17/MathT5-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 "jmeadows17/MathT5-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": "jmeadows17/MathT5-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jmeadows17/MathT5-large with Docker Model Runner:
```
docker model run hf.co/jmeadows17/MathT5-large
```

jmeadows17 commited on Jul 21, 2023

Commit

7073d08

1 Parent(s): 41c3742

Update README.md

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@@ -27,4 +27,6 @@ then obtain (- \\sin{(q)})^{q} (\\frac{d}{d q} \\cos{(q)})^{q} = (- \\sin{(q)})^
 Output derivations are equations separated by "and".
 Use ```"jmeadows17/MathT5-base"``` for the lightweight version.

 Output derivations are equations separated by "and".
+Additional prompts can be found in "training_prompts.json" alongside the model files.
 Use ```"jmeadows17/MathT5-base"``` for the lightweight version.