Performance LLMs - Fine tuned
Collection
27 items
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To understand the pun intended, lookup my 3b Deacon model.
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.
### Instruction:
How do you fine tune a large language model?
### Response:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 61.28 |
| AI2 Reasoning Challenge (25-Shot) | 60.75 |
| HellaSwag (10-Shot) | 81.74 |
| MMLU (5-Shot) | 60.70 |
| TruthfulQA (0-shot) | 58.49 |
| Winogrande (5-shot) | 76.80 |
| GSM8k (5-shot) | 29.19 |
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Dataset used to train KnutJaegersberg/Deacon-20B
Collection including KnutJaegersberg/Deacon-20B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.750
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.740
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.700
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard58.490
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard76.800
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard29.190