# appvoid/palmer-x-002-GGUF

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


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



## Original Model Card:
![palmer](https://huggingface.co/appvoid/palmer-002-2312/resolve/main/_4a591880-0e06-45ad-9a6d-81302da72c2e.jpeg?download=true)
# x-002
This is an incremental model update on `palmer-002` using dpo technique. X means dpo+sft spinoff.

### 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|
|palmer-x-002|0.3224|0.5941|**0.7383**|**0.5912**|

### training
~500 dpo samples as experimental data to check on improvements. It seems like data is making it better on some benchmarks while also degrading quality on others.

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

As you can notice, the model actually completes by default questions that are the most-likely to be asked, which is good because most people will use it to answer as a chatbot.
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{"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": "palmer-x-002", "base_model": "appvoid/palmer-x-002", "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/palmer-x-002 license:apache-2.0 region:us