Instructions to use llmware/slim-emotions-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-emotions-onnx with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llmware/slim-emotions-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| inference: false | |
| tags: [green, p1, llmware-fx, ov, emerald] | |
| # slim-emotions-ov | |
| **slim-emotions-ov** is a specialized function calling model that classifies the emotion of a given text context passage, and generates a python dictionary with an "emotions" key and a value of the assessed emotion, e.g., ["surprised"]. | |
| This is an OpenVino int4 quantized version of slim-emotions, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU. | |
| ### Model Description | |
| - **Developed by:** llmware | |
| - **Model type:** tinyllama | |
| - **Parameters:** 1.1 billion | |
| - **Model Parent:** llmware/slim-emotions | |
| - **Language(s) (NLP):** English | |
| - **License:** Apache 2.0 | |
| - **Uses:** Emotions classifier designed for Agent-based multi-step workflows | |
| - **RAG Benchmark Accuracy Score:** NA | |
| - **Quantization:** int4 | |
| ## Model Card Contact | |
| [llmware on github](https://www.github.com/llmware-ai/llmware) | |
| [llmware on hf](https://www.huggingface.co/llmware) | |
| [llmware website](https://www.llmware.ai) | |