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
File size: 911 Bytes
2b1d5b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"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}
]
}
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