appvoid
/

palmer-002

@@ -8,10 +8,11 @@ datasets:
 ---
 ![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
 ```
    Model           ARC_C   HellaSwag  PIQA  Winogrande Average
 tinyllama-2      | 0.2807 | 0.5463 | 0.7067 | 0.5683 | 0.5255 |
@@ -22,16 +23,15 @@ tinyllama-3      | 0.3029 | 0.5935 | 0.7329 | 0.5959 | 0.5563 |
 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 |
-# note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals.
 ```
 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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 ---
 ![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 |
 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 🚀
 ```
 <a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 48px !important;width: 180px !important; filter: invert(70%);" ></a>