Instructions to use procedure2012/Titan-Math-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Titan-Math-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Titan-Math-Pro")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Titan-Math-Pro") model = AutoModel.from_pretrained("procedure2012/Titan-Math-Pro") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("procedure2012/Titan-Math-Pro")
model = AutoModel.from_pretrained("procedure2012/Titan-Math-Pro")Titan-Math-Pro
1. Introduction
Titan-Math-Pro is a mathematics-first model trained on verified proofs and competition problems with process supervision.
2. Evaluation Results
Comprehensive Benchmark Results
| Benchmark | NumeriX | Titan-base | ProofNet | Titan-Math-Pro | |
|---|---|---|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.589 | 0.595 | 0.577 | 0.633 |
| Logical Reasoning | 0.816 | 0.818 | 0.793 | 0.850 | |
| Common Sense | 0.749 | 0.735 | 0.776 | 0.783 | |
| Language Understanding | Reading Comprehension | 0.707 | 0.731 | 0.743 | 0.760 |
| Question Answering | 0.595 | 0.595 | 0.642 | 0.649 | |
| Text Classification | 0.815 | 0.817 | 0.831 | 0.849 | |
| Sentiment Analysis | 0.779 | 0.780 | 0.765 | 0.818 | |
| Generation Tasks | Code Generation | 0.703 | 0.682 | 0.702 | 0.730 |
| Creative Writing | 0.693 | 0.670 | 0.658 | 0.700 | |
| Dialogue Generation | 0.683 | 0.677 | 0.656 | 0.702 | |
| Summarization | 0.793 | 0.765 | 0.771 | 0.805 | |
| Specialized Capabilities | Translation | 0.802 | 0.815 | 0.814 | 0.826 |
| Knowledge Retrieval | 0.667 | 0.672 | 0.687 | 0.718 | |
| Instruction Following | 0.756 | 0.750 | 0.741 | 0.798 | |
| Safety Evaluation | 0.762 | 0.739 | 0.734 | 0.779 |
Overall Performance Summary
The Titan-Math-Pro demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.
3. Chat Website & API Platform
We offer a chat interface and API for you to interact with Titan-Math-Pro. Please check our official website for more details.
4. How to Run Locally
Please refer to our code repository for more information about running Titan-Math-Pro locally.
Temperature
We recommend setting the temperature parameter to 0.6.
5. License
This repository is released under the mit license. The model supports commercial use.
6. Contact
If you have any questions, please contact us at math@titan-compute.com.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Titan-Math-Pro")