# appvoid/no-prompt-1.3b-GGUF

Quantized GGUF model files for [no-prompt-1.3b](https://huggingface.co/appvoid/no-prompt-1.3b) from [appvoid](https://huggingface.co/appvoid)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [no-prompt-1.3b.fp16.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.fp16.gguf) | fp16 | 2.69 GB  |
| [no-prompt-1.3b.q2_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q2_k.gguf) | q2_k | 631.52 MB  |
| [no-prompt-1.3b.q3_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q3_k_m.gguf) | q3_k_m | 704.72 MB  |
| [no-prompt-1.3b.q4_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q4_k_m.gguf) | q4_k_m | 873.27 MB  |
| [no-prompt-1.3b.q5_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q5_k_m.gguf) | q5_k_m | 1.00 GB  |
| [no-prompt-1.3b.q6_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q6_k.gguf) | q6_k | 1.17 GB  |
| [no-prompt-1.3b.q8_0.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q8_0.gguf) | q8_0 | 1.43 GB  |



## Original Model Card:
![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.

<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>
{"language": ["en"], "license": "apache-2.0", "tags": ["gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "datasets": ["appvoid/no-prompt-15k"], "model_name": "no-prompt-1.3b", "base_model": "appvoid/no-prompt-1.3b", "inference": false, "model_creator": "appvoid", "pipeline_tag": "text-generation", "quantized_by": "afrideva"}
gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0 text-generation en dataset:appvoid/no-prompt-15k base_model:appvoid/no-prompt-1.3b license:apache-2.0 region:us