Instructions to use procedure2012/Nimbus-Chat-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Nimbus-Chat-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Nimbus-Chat-Mini")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Nimbus-Chat-Mini") model = AutoModel.from_pretrained("procedure2012/Nimbus-Chat-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| # Nimbus-Chat-Mini | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="Nimbus-Chat-Mini" /> | |
| </div> | |
| <hr> | |
| ## 1. Introduction | |
| Nimbus-Chat-Mini is a compact assistant for on-device dialogue. Despite its size it shows strong instruction following after our distillation pipeline. | |
| ## 2. Evaluation Results | |
| ### Comprehensive Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | TinyTalk | Nimbus-0.5 | Breeze | Nimbus-Chat-Mini | | |
| |---|---|---|---|---|---| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.603 | 0.621 | 0.590 | 0.633 | | |
| | | Logical Reasoning | 0.798 | 0.835 | 0.794 | 0.850 | | |
| | | Common Sense | 0.774 | 0.774 | 0.766 | 0.783 | | |
| | **Language Understanding** | Reading Comprehension | 0.714 | 0.719 | 0.735 | 0.760 | | |
| | | Question Answering | 0.593 | 0.640 | 0.607 | 0.649 | | |
| | | Text Classification | 0.807 | 0.822 | 0.830 | 0.849 | | |
| | | Sentiment Analysis | 0.806 | 0.762 | 0.772 | 0.818 | | |
| | **Generation Tasks** | Code Generation | 0.684 | 0.713 | 0.688 | 0.730 | | |
| | | Creative Writing | 0.653 | 0.655 | 0.689 | 0.700 | | |
| | | Dialogue Generation | 0.681 | 0.647 | 0.664 | 0.702 | | |
| | | Summarization | 0.763 | 0.790 | 0.780 | 0.805 | | |
| | **Specialized Capabilities** | Translation | 0.788 | 0.768 | 0.803 | 0.826 | | |
| | | Knowledge Retrieval | 0.685 | 0.679 | 0.684 | 0.718 | | |
| | | Instruction Following | 0.771 | 0.740 | 0.776 | 0.798 | | |
| | | Safety Evaluation | 0.728 | 0.767 | 0.729 | 0.779 | | |
| </div> | |
| ### Overall Performance Summary | |
| The Nimbus-Chat-Mini 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 Nimbus-Chat-Mini. Please check our official website for more details. | |
| ## 4. How to Run Locally | |
| Please refer to our code repository for more information about running Nimbus-Chat-Mini 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 team@nimbus.chat. | |