Performance LLMs - Fine tuned
Collection
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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. | 72.16 |
| AI2 Reasoning Challenge (25-Shot) | 63.05 |
| HellaSwag (10-Shot) | 84.67 |
| MMLU (5-Shot) | 73.95 |
| TruthfulQA (0-shot) | 58.11 |
| Winogrande (5-shot) | 80.82 |
| GSM8k (5-shot) | 72.33 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard63.050
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.670
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard73.950
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard58.110
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.820
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard72.330