Update README.md

README.md

@@ -6,10 +6,10 @@ 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.
+![palmer](https://huggingface.co/appvoid/no-prompt-1.3b/resolve/main/_ccd1a5dd-2ddc-4d5a-8163-fd6d1b39f5f4.jpeg?download=true)
+# no-prompt
+### a sheared-llama-1.3b fine-tuning
+This model uses an 1.3 billion parameters model as base to be further fine-tuned on the same data as palmer. It works pretty good and even surpasses sota model on `hellaswag`.
 
 ### evaluation
 |Model|	ARC_C|	HellaSwag|	PIQA|	Winogrande|
@@ -20,7 +20,7 @@ palmer is a series of ~1b parameters language models fine-tuned to be used as ba
 |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!
+This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard.
 
 ### 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.