Performance LLMs - Fine tuned
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Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact internlm@pjlab.org.cn.
Prompt Example:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User:
How do you fine tune a large language model?
### Assistant:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 71.40 |
| AI2 Reasoning Challenge (25-Shot) | 63.91 |
| HellaSwag (10-Shot) | 83.11 |
| MMLU (5-Shot) | 67.40 |
| TruthfulQA (0-shot) | 57.29 |
| Winogrande (5-shot) | 84.61 |
| GSM8k (5-shot) | 72.10 |
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Dataset used to train KnutJaegersberg/Deita-20b
Collection including KnutJaegersberg/Deita-20b
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard63.910
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.110
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard67.400
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard57.290
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.610
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard72.100