README
This is a Hugging Face transformers-compatible conversion of the original dense 2.7B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 33.67 |
| ARC (25-shot) | 33.79 |
| HellaSwag (10-shot) | 65.74 |
| MMLU (5-shot) | 26.44 |
| TruthfulQA (0-shot) | 34.57 |
| Winogrande (5-shot) | 63.93 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 11.24 |
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README
This is a Hugging Face transformers-compatible conversion of the original dense 2.7B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 33.67 |
| ARC (25-shot) | 33.79 |
| HellaSwag (10-shot) | 65.74 |
| MMLU (5-shot) | 26.44 |
| TruthfulQA (0-shot) | 34.57 |
| Winogrande (5-shot) | 63.93 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 11.24 |