Instructions to use jmeadows17/MathT5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
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README.md
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license: openrail
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It outperforms GPT-4 and ChatGPT (paper link soon) on a derivation generation task in ROUGE, BLEU, BLEURT, and GLEU, and shows some generalisation capabilities.
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It was trained on 155 physics symbols, but struggles with out-of-vocabulary symbols.
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license: openrail
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pipeline_tag: text-generation
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**Overview**
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MathT5-large is a version of FLAN-T5-large fine-tuned for 25 epochs on 15K (LaTeX) synthetic mathematical derivations (containing 5 - 9 equations), that were generated using a symbolic solver (SymPy).
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It outperforms GPT-4 and ChatGPT (paper link soon) on a derivation generation task in ROUGE, BLEU, BLEURT, and GLEU scores, and shows some generalisation capabilities.
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It was trained on 155 physics symbols, but struggles with out-of-vocabulary symbols.
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**Example prompt:**
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```prompt = "Given \\cos{(q)} = \\theta{(q)},
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then derive - \\sin{(q)} = \\frac{d}{d q} \\theta{(q)},
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then obtain (- \\sin{(q)})^{q} (\\frac{d}{d q} \\cos{(q)})^{q} = (- \\sin{(q)})^{2 q}"```
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**To use MathT5 easily:**
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1. Download ```MathT5.py``` to your working directory.
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2. ```from MathT5 import load_model, inference```
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3. ```tokenizer, model = load_model("jmeadows17/MathT5-large")```
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4. ```inference(prompt, tokenizer, model)```
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