Instructions to use procedure2012/Helios-Reasoner-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Helios-Reasoner-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Helios-Reasoner-7B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Helios-Reasoner-7B") model = AutoModel.from_pretrained("procedure2012/Helios-Reasoner-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model": "Helios-Reasoner-7B", | |
| "checkpoint": "step_1200", | |
| "overall_eval_accuracy": 0.725, | |
| "num_benchmarks": 15, | |
| "benchmarks": [ | |
| {"name": "code_generation", "score": 0.6735}, | |
| {"name": "common_sense", "score": 0.75}, | |
| {"name": "creative_writing", "score": 0.6357}, | |
| {"name": "dialogue_generation", "score": 0.6602}, | |
| {"name": "instruction_following", "score": 0.77}, | |
| {"name": "knowledge_retrieval", "score": 0.6879}, | |
| {"name": "logical_reasoning", "score": 0.8383}, | |
| {"name": "math_reasoning", "score": 0.5727}, | |
| {"name": "question_answering", "score": 0.6179}, | |
| {"name": "reading_comprehension", "score": 0.7176}, | |
| {"name": "safety_evaluation", "score": 0.75}, | |
| {"name": "sentiment_analysis", "score": 0.8}, | |
| {"name": "summarization", "score": 0.7786}, | |
| {"name": "text_classification", "score": 0.8377}, | |
| {"name": "translation", "score": 0.8111} | |
| ] | |
| } | |