--- 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 🧪 note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals ``` Model ARC_C HellaSwag PIQA Winogrande Average tinyllama-2 | 0.2807 | 0.5463 | 0.7067 | 0.5683 | 0.5255 | palmer-001 | 0.2807 | 0.5524 | 0.7106 | 0.5896 | 0.5333 | babbage-001 | 0.2944 | 0.5448 | 0.7410 | 0.5935 | 0.5434 | deacon-1b | 0.2944 | 0.5727 | 0.7040 | 0.5801 | 0.5434 | tinyllama-2.5 | 0.3191 | 0.5896 | 0.7307 | 0.5872 | 0.5566 | palmer-002 | 0.3242 | 0.5956 | 0.7345 | 0.5888 | 0.5607 | babbage-002 | 0.3285 | 0.6380 | 0.7606 | 0.6085 | 0.5839 | ``` 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 🚀 ```