---
base_model: appvoid/palmer-002
datasets:
- appvoid/no-prompt-15k
inference: false
language:
- en
license: apache-2.0
model_creator: appvoid
model_name: palmer-002
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/palmer-002-GGUF

Quantized GGUF model files for [palmer-002](https://huggingface.co/appvoid/palmer-002) from [appvoid](https://huggingface.co/appvoid)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [palmer-002.fp16.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.fp16.gguf) | fp16 | 2.20 GB  |
| [palmer-002.q2_k.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q2_k.gguf) | q2_k | 483.12 MB  |
| [palmer-002.q3_k_m.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q3_k_m.gguf) | q3_k_m | 550.82 MB  |
| [palmer-002.q4_k_m.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q4_k_m.gguf) | q4_k_m | 668.79 MB  |
| [palmer-002.q5_k_m.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q5_k_m.gguf) | q5_k_m | 783.02 MB  |
| [palmer-002.q6_k.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q6_k.gguf) | q6_k | 904.39 MB  |
| [palmer-002.q8_0.gguf](https://huggingface.co/afrideva/palmer-002-GGUF/resolve/main/palmer-002.q8_0.gguf) | q8_0 | 1.17 GB  |



## Original Model Card:
![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|
|------|-----|-----------|------|-------------|
|tinyllama-2t|	0.2807|	0.5463|	0.7067|	0.5683|
|palmer-001|	0.2807|	0.5524|	0.7106|	**0.5896**|
|tinyllama-2.5t|0.3191|0.5896|	0.7307|	0.5872|
|palmer-002|**0.3242**|**0.5956**|**0.7345**|0.5888|


### 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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