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. | 56.43 |
| AI2 Reasoning Challenge (25-Shot) | 46.08 |
| HellaSwag (10-Shot) | 71.81 |
| MMLU (5-Shot) | 55.46 |
| TruthfulQA (0-shot) | 50.23 |
| Winogrande (5-shot) | 66.14 |
| GSM8k (5-shot) | 48.90 |
- Downloads last month
- 82
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.
Model tree for KnutJaegersberg/Deita-4b
Dataset used to train KnutJaegersberg/Deita-4b
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard46.080
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard71.810
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard55.460
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard50.230
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard66.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard48.900