license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- appvoid/no-prompt-15k
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 600m llama 2 model (since is 4bit quantized) 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
palmer-001 | 0.2807 | 0.5524 | 0.7106 | 0.5896 | 0.5333 |
palmer-003-turbo | 0.3106 | 0.5806 | 0.7247 | 0.5951 | 0.5527 |
palmer-002 | 0.3242 | 0.5956 | 0.7345 | 0.5888 | 0.5607 |
This model is as good as tinyllama base while being half the size.
training 🦾
Training took 1.5 rtx 2060 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 🚀
Note
As of today 1/4/2024 is still not possible to convert to gguf, see more here.
