Instructions to use procedure2012/Vega-Instruct-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Vega-Instruct-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Vega-Instruct-13B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Vega-Instruct-13B") model = AutoModel.from_pretrained("procedure2012/Vega-Instruct-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-4.0 | |
| library_name: transformers | |
| # Vega-Instruct-13B | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="Vega-Instruct-13B" /> | |
| </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 | |
| Vega-Instruct-13B is tuned for faithful instruction following and low hallucination. This release adds robust function-calling support. | |
| <p align="center"> | |
| <img width="80%" src="figures/fig3.png"> | |
| </p> | |
| ## 2. Evaluation Results | |
| ### Comprehensive Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | Polaris | Vega-7B | Castor-13B | Vega-Instruct-13B | | |
| |---|---|---|---|---|---| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.573 | 0.569 | 0.589 | 0.621 | | |
| | | Logical Reasoning | 0.803 | 0.815 | 0.824 | 0.849 | | |
| | | Common Sense | 0.750 | 0.763 | 0.728 | 0.777 | | |
| | **Language Understanding** | Reading Comprehension | 0.693 | 0.742 | 0.697 | 0.752 | | |
| | | Question Answering | 0.620 | 0.593 | 0.634 | 0.643 | | |
| | | Text Classification | 0.799 | 0.824 | 0.824 | 0.848 | | |
| | | Sentiment Analysis | 0.772 | 0.757 | 0.776 | 0.815 | | |
| | **Generation Tasks** | Code Generation | 0.682 | 0.711 | 0.699 | 0.720 | | |
| | | Creative Writing | 0.654 | 0.641 | 0.654 | 0.688 | | |
| | | Dialogue Generation | 0.684 | 0.646 | 0.645 | 0.694 | | |
| | | Summarization | 0.757 | 0.755 | 0.754 | 0.800 | | |
| | **Specialized Capabilities** | Translation | 0.817 | 0.782 | 0.774 | 0.823 | | |
| | | Knowledge Retrieval | 0.661 | 0.677 | 0.667 | 0.712 | | |
| | | Instruction Following | 0.773 | 0.770 | 0.772 | 0.793 | | |
| | | Safety Evaluation | 0.735 | 0.733 | 0.736 | 0.773 | | |
| </div> | |
| ### Overall Performance Summary | |
| The Vega-Instruct-13B 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 Vega-Instruct-13B. Please check our official website for more details. | |
| ## 4. How to Run Locally | |
| Please refer to our code repository for more information about running Vega-Instruct-13B locally. | |
| ### Temperature | |
| We recommend setting the temperature parameter to 0.6. | |
| ## 5. License | |
| This repository is released under the cc-by-4.0 license. The model supports commercial use. | |
| ## 6. Contact | |
| If you have any questions, please contact us at contact@vega.ai. | |