Base model is appvoid/palmer-001, fine tuned for 3 epochs with Neftune.
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. | 35.21 |
| AI2 Reasoning Challenge (25-Shot) | 32.42 |
| HellaSwag (10-Shot) | 58.62 |
| MMLU (5-Shot) | 24.89 |
| TruthfulQA (0-shot) | 35.05 |
| Winogrande (5-shot) | 59.59 |
| GSM8k (5-shot) | 0.68 |
- Downloads last month
- 1,399
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the model is not deployed on the HF Inference API.
Model tree for KnutJaegersberg/Deacon-1b
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard32.420
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard58.620
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard24.890
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard35.050
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard59.590
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.680