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---
base_model: appvoid/no-prompt-1.3b
datasets:
- appvoid/no-prompt-15k
inference: false
language:
- en
license: apache-2.0
model_creator: appvoid
model_name: no-prompt-1.3b
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# 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:
# 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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