A Reproduction of OpenLLaMA using 128 H100 GPUs in Bfloat16.
The pretrain data consists of Falcon, Starcoder, and the wikipedia, arxiv, books, stackexchange from RedPajama. In total, this encompassed nearly 1 trillion tokens.
The model was trained over a single epoch, incorporating 2000 warm-up steps and a cosine learning rate schedule, starting at 3e-5 with 4M batch size.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 47.09 |
| AI2 Reasoning Challenge (25-Shot) | 46.16 |
| HellaSwag (10-Shot) | 76.40 |
| MMLU (5-Shot) | 42.82 |
| TruthfulQA (0-shot) | 36.65 |
| Winogrande (5-shot) | 70.88 |
| GSM8k (5-shot) | 9.63 |
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Datasets used to train itsliupeng/openllama-7b-base
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard46.160
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard76.400
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard42.820
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard36.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard70.880
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard9.630