![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|
|------|-----|-----------|------|-------------|
|tinyllama-2t| 0.2807| 0.5463| 0.7067| 0.5683|
|palmer-001  | 0.2807| 0.5524| 0.7106| 0.5896|
|sheared-1.3b| 0.2910| 0.5935| 0.7339| 0.5809|
|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.

### training
Training took ~5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. no-prompt was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.

### prompt
```
no prompt
```

### limitations
Hallucinations are frequent, just as any transformer model this size.

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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