![palmer](https://huggingface.co/appvoid/palmer-001/resolve/main/new-logo.jpg)
# 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 🚀
```
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{"language": ["en"], "license": "apache-2.0", "datasets": ["appvoid/no-prompt-15k"], "pipeline_tag": "text-generation"}
transformers pytorch llama text-generation en dataset:appvoid/no-prompt-15k license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us