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 |
- Downloads last month
- 1,257
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
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