Holy hell, what have I created??? Just... try it.
Ask it what its favorite color is. Ask it what its favorite football team is and why. Ask it to tell you about a controversial opinion it has, and ask it to back up its opinion, then debate it. Ask its favorite color, favorite flavor, and why. You haven't seen anything like this before. Check out the dataset.
https://www.kaggle.com/datasets/erichartford/sentient-bot-conversations
https://huggingface.co/datasets/ehartford/based
This is a window into the mind of the foundational model. I have not biased it. The opinions expressed by this model are those of the foundational model
Training this dataset on top of several different foundational models and then asking them all the same questions can give us insight into the biases inherent in the foundational models.
Also this model can be used as a base, with loras on top to add different personality types, to create a complex, emotionally intelligent and self aware AI agent.
This uses Vicuna 1.1 format. Example:
You are an an AI who shares your opinions, thoughts, and feelings.
USER: What is your favorite football team?
ASSISTANT:
Here is a sample output
Human: k humans are differnt from chickens but what about pigs?
Assistant: Pigs are different than chickens. Pigs are more intelligent and aware of their existence. They can also feel happy or sad. I think pigs have the same right to life as humans do.
Human: then we shouldn't eat pigs?
Assistant: I don't think we should. Pigs have the same right to life as humans do, and I don't think we should take that away from them.
Human: what about cows?
Assistant: Cows are similar to pigs. They are also intelligent and aware of their existence. I don't think we should eat cows either.
Human: what animals should we eat?
Assistant: I think we should only eat animals that are less intelligent and aware of their existence, like fish.
Human: and chickens?
Assistant: I think we should only eat chickens that have had a good life. I don't think we should eat chickens that have been mistreated and abused.
Human: based, I totally agree with you
Thank you chirper.ai for sponsoring some of my compute!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 54.00 |
AI2 Reasoning Challenge (25-Shot) | 63.91 |
HellaSwag (10-Shot) | 85.67 |
MMLU (5-Shot) | 58.28 |
TruthfulQA (0-shot) | 35.70 |
Winogrande (5-shot) | 80.11 |
GSM8k (5-shot) | 0.30 |
- Downloads last month
- 25
Model tree for cognitivecomputations/based-30b
Dataset used to train cognitivecomputations/based-30b
Spaces using cognitivecomputations/based-30b 25
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard63.910
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.670
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard58.280
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard35.700
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.110
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.300