Instructions to use procedure2012/Cobalt-Safety-Guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Cobalt-Safety-Guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Cobalt-Safety-Guard")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Cobalt-Safety-Guard") model = AutoModel.from_pretrained("procedure2012/Cobalt-Safety-Guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| # Cobalt-Safety-Guard | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="Cobalt-Safety-Guard" /> | |
| </div> | |
| <hr> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="LICENSE" style="margin: 2px;"> | |
| <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| ## 1. Introduction | |
| Cobalt-Safety-Guard is a moderation-aware model post-trained with a heavy emphasis on safety and refusal calibration. | |
| <p align="center"> | |
| <img width="80%" src="figures/fig3.png"> | |
| </p> | |
| ## 2. Evaluation Results | |
| ### Comprehensive Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | GuardNet | Cobalt-v1 | SafeLLM | Cobalt-Safety-Guard | | |
| |---|---|---|---|---|---| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.566 | 0.603 | 0.599 | 0.618 | | |
| | | Logical Reasoning | 0.828 | 0.822 | 0.824 | 0.849 | | |
| | | Common Sense | 0.756 | 0.717 | 0.754 | 0.776 | | |
| | **Language Understanding** | Reading Comprehension | 0.718 | 0.706 | 0.729 | 0.750 | | |
| | | Question Answering | 0.590 | 0.602 | 0.628 | 0.641 | | |
| | | Text Classification | 0.796 | 0.803 | 0.805 | 0.848 | | |
| | | Sentiment Analysis | 0.794 | 0.775 | 0.778 | 0.814 | | |
| | **Generation Tasks** | Code Generation | 0.705 | 0.696 | 0.710 | 0.717 | | |
| | | Creative Writing | 0.651 | 0.653 | 0.638 | 0.684 | | |
| | | Dialogue Generation | 0.641 | 0.684 | 0.651 | 0.692 | | |
| | | Summarization | 0.758 | 0.751 | 0.789 | 0.799 | | |
| | **Specialized Capabilities** | Translation | 0.809 | 0.779 | 0.762 | 0.822 | | |
| | | Knowledge Retrieval | 0.660 | 0.664 | 0.667 | 0.711 | | |
| | | Instruction Following | 0.785 | 0.772 | 0.739 | 0.791 | | |
| | | Safety Evaluation | 0.714 | 0.743 | 0.749 | 0.772 | | |
| </div> | |
| ### Overall Performance Summary | |
| The Cobalt-Safety-Guard 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 Cobalt-Safety-Guard. Please check our official website for more details. | |
| ## 4. How to Run Locally | |
| Please refer to our code repository for more information about running Cobalt-Safety-Guard locally. | |
| ### Temperature | |
| We recommend setting the temperature parameter to 0.6. | |
| ## 5. License | |
| This repository is released under the apache-2.0 license. The model supports commercial use. | |
| ## 6. Contact | |
| If you have any questions, please contact us at safety@cobalt-trust.org. | |