--- license: apache-2.0 language: - en datasets: - appvoid/no-prompt-15k pipeline_tag: text-generation --- ![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 1.3b llama 2 model so you can use it with your favorite tools/frameworks. ### evaluation |Model| ARC_C| HellaSwag| PIQA| Winogrande| |------|-----|-----------|------|-------------| |tinyllama-2t| 0.2807| 0.5463| 0.7067| 0.5683| |palmer-001 | 0.2807| 0.5524| 0.7106| 0.5896| |sheared-1.3b| 0.2910| 0.5935| 0.7339| 0.5809| |no-prompt-1.3b| 0.3157| **0.6022**| 0.7334| 0.5864| |falcon-rw-1b-instruct-openorca (sota) | **0.3362**| 0.5997| **0.7394**| **0.6148**| This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard. Wait for what it's coming! ### training Training took ~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 ``` ### limitations Hallucinations are frequent, just as any transformer model this size.