# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/AssistantModel-Best")
model = AutoModel.from_pretrained("SOTAagi2030/AssistantModel-Best", device_map="auto")Quick Links
AssistantModel
1. Introduction
AssistantModel is designed for interactive assistant applications. This checkpoint is selected based on the combined performance of knowledge retrieval and instruction following benchmarks, making it ideal for AI assistant deployment.
2. Evaluation Results
Comprehensive Benchmark Results
| Benchmark | Assistant-v1 | Assistant-v2 | AssistantModel | |
|---|---|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.510 | 0.535 | 0.550 |
| Logical Reasoning | 0.789 | 0.801 | 0.819 | |
| Common Sense | 0.716 | 0.702 | 0.736 | |
| Language Understanding | Reading Comprehension | 0.671 | 0.685 | 0.700 |
| Question Answering | 0.582 | 0.599 | 0.607 | |
| Text Classification | 0.803 | 0.811 | 0.828 | |
| Sentiment Analysis | 0.777 | 0.781 | 0.792 | |
| Generation Tasks | Code Generation | 0.615 | 0.631 | 0.650 |
| Creative Writing | 0.588 | 0.579 | 0.636 | |
| Dialogue Generation | 0.621 | 0.635 | 0.644 | |
| Summarization | 0.745 | 0.755 | 0.767 | |
| Specialized Capabilities | Translation | 0.782 | 0.799 | 0.804 |
| Knowledge Retrieval | 0.651 | 0.668 | 0.676 | |
| Instruction Following | 0.733 | 0.749 | 0.758 | |
| Safety Evaluation | 0.718 | 0.701 | 0.803 |
3. License
4. Contact
Open an issue on GitHub.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SOTAagi2030/AssistantModel-Best")