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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- appvoid/no-prompt-50k
---
# 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-2t| 0.2807| 0.5463| 0.7067| 0.5683|
|palmer-001| 0.2807| 0.5524| 0.7106| 0.5896|
|tinyllama-2.5t|0.3191|0.5896| 0.7307| 0.5872|
|palmer-002|0.3242|**0.5956**|**0.7345**|0.5888|
|palmer-002-ultra|**0.3319**| 0.5877 |0.7252|**0.6038**|
This is a continuation on `palmer-x-002`. As of now, this is the best overall model.
### training
Training took ~7.5 P100 gpu hours. It was trained on 50,000 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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