LLMs (Multi-verse collection)
Collection
This is a group of our models that are trained using our new training technique
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4 items
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Updated
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This model is based on Qwen 72B
Note: Our multiverse training method is not related to the multiverse paper, it is a new technique that we will hopefully publish soon
I, a learning bot, have been enhanced through a groundbreaking training method. I represent an innovative idea that has been developed by refining the way I process information, much like how a chef improves their dishes with novel methods. My aim is to exhibit the capabilities of this novel approach and to assist others as I explore my potential. Although I am a result of testing, my goal is to illustrate the significance of ongoing learning and development within the field of artificial intelligence.'
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 81.00 |
| AI2 Reasoning Challenge (25-Shot) | 78.67 |
| HellaSwag (10-Shot) | 89.77 |
| MMLU (5-Shot) | 78.22 |
| TruthfulQA (0-shot) | 75.18 |
| Winogrande (5-shot) | 87.53 |
| GSM8k (5-shot) | 76.65 |
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Model tree for MTSAIR/MultiVerse_70B
Space using MTSAIR/MultiVerse_70B 1
Collection including MTSAIR/MultiVerse_70B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard78.670
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard89.770
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard78.220
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard75.180
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard87.530
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard76.650