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:

License Link: https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	52.35
AI2 Reasoning Challenge (25-Shot)	44.71
HellaSwag (10-Shot)	70.39
MMLU (5-Shot)	52.79
TruthfulQA (0-shot)	39.61
Winogrande (5-shot)	65.27
GSM8k (5-shot)	41.32

Downloads last month: 113

Safetensors

Model size

3B params

Tensor type

F16

Model tree for KnutJaegersberg/Deita-2b

Quantizations

2 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

44.710
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

70.390
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

52.790
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

39.610
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

65.270
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

41.320