--- license: apache-2.0 language: - en pipeline_tag: text-generation datasets: - appvoid/no-prompt-15k --- ![palmer](https://huggingface.co/appvoid/palmer-001/resolve/main/palmer.jpeg) # palmer ### a better base model palmer is a series of ~1b parameters language models fine-tuned to be used as base models instead of using custom prompts for tasks. This means that it can be further fine-tuned on more data with custom prompts as usual or be used for downstream tasks as any base model you can get. The model has the best of both worlds: some "bias" to act as an assistant, but also the abillity to predict the next-word from its internet knowledge base. It's a 1.1b llama 2 model so you can use it with your favorite tools/frameworks. ### evaluation |Model| ARC_C| HellaSwag| PIQA| Winogrande| |------|-----|-----------|------|-------------| |tinyllama-2 | 0.2807 |0.5463| 0.7067 | 0.5683| |palmer-001 | 0.2807 |0.5524| 0.7106 | 0.5896| |tinyllama-2.5| 0.3191 |0.5896| 0.7307 | 0.5872| |tinyllama-3 | 0.3029 |0.5935| 0.7329 | **0.5959**| |palmer-002|**0.3242**|**0.5956**|**0.7345**| 0.5888| This model shows exceptional performance and as of now is the best tinyllama-size base model. Furthermore, this proves LIMA paper point and serves as a good open-source alternative to openai's `babbage-002`. ### training Training took ~3.5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. palmer was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible. ### prompt ``` no prompt ```