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