---
language:
- sr
license: mit
tags:
- text-generation-inference
- transformers
- mistral
- gguf
base_model: datatab/Yugo55A-GPT
datasets:
- datatab/ultrafeedback_binarized
- datatab/open-orca-slim-serbian
- datatab/alpaca-cleaned-serbian-full
---

# Yugo55A-GPT.GGUF

- **Developed by:** datatab
- **License:** MIT
- **Quantized from model :** [datatab/Yugo55A-GPT](https://huggingface.co/datatab/Yugo55A-GPT)


### Full Weights Model
>  [datatab/Yugo55A-GPT](https://huggingface.co/datatab/Yugo55A-GPT).



## 🏆 Results 
> Results obtained through the Serbian LLM evaluation, released by Aleksa Gordić: [serbian-llm-eval](https://github.com/gordicaleksa/serbian-llm-eval)
> * Evaluation was conducted on a 4-bit version of the model due to hardware resource constraints.

<table>
  <tr>
    <th>MODEL</th>
    <th>ARC-E</th>
    <th>ARC-C</th>
    <th>Hellaswag</th>
    <th>BoolQ</th>
    <th>Winogrande</th>
    <th>OpenbookQA</th>
    <th>PiQA</th>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datatab/Yugo55-GPT-v4-4bit/">*Yugo55-GPT-v4-4bit</a></td>
    <td>51.41</td>
    <td>36.00</td>
    <td>57.51</td>
    <td>80.92</td>
    <td><strong>65.75</strong></td>
    <td>34.70</td>
    <td><strong>70.54</strong></td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datatab/Yugo55A-GPT/">Yugo55A-GPT</a></td>
    <td><strong>51.52</strong></td>
    <td><strong>37.78</strong></td>
    <td><strong>57.52</strong></td>
    <td><strong>84.40</strong></td>
    <td>65.43</td>
    <td><strong>35.60</strong></td>
    <td>69.43</td>
  </tr>
</table>

# Quant. preference

| Quant.           | Description                                                                           |
|---------------|---------------------------------------------------------------------------------------|
| not_quantized | Recommended. Fast conversion. Slow inference, big files.                              |
| fast_quantized| Recommended. Fast conversion. OK inference, OK file size.                             |
| quantized     | Recommended. Slow conversion. Fast inference, small files.                            |
| f32           | Not recommended. Retains 100% accuracy, but super slow and memory hungry.             |
| f16           | Fastest conversion + retains 100% accuracy. Slow and memory hungry.                   |
| q8_0          | Fast conversion. High resource use, but generally acceptable.                         |
| q4_k_m        | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K |
| q5_k_m        | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K |
| q2_k          | Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.|
| q3_k_l        | Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K  |
| q3_k_m        | Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K  |
| q3_k_s        | Uses Q3_K for all tensors                                                             |
| q4_0          | Original quant method, 4-bit.                                                         |
| q4_1          | Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.|
| q4_k_s        | Uses Q4_K for all tensors                                                             |
| q4_k          | alias for q4_k_m                                                                      |
| q5_k          | alias for q5_k_m                                                                      |
| q5_0          | Higher accuracy, higher resource usage and slower inference.                          |
| q5_1          | Even higher accuracy, resource usage and slower inference.                            |
| q5_k_s        | Uses Q5_K for all tensors                                                             |
| q6_k          | Uses Q8_K for all tensors                                                             |
| iq2_xxs       | 2.06 bpw quantization                                                                 |
| iq2_xs        | 2.31 bpw quantization                                                                 |
| iq3_xxs       | 3.06 bpw quantization                                                                 |
| q3_k_xs       | 3-bit extra small quantization                                                        |