---
language:
- sr
license: apache-2.0
tags:
- text-generation-inference
- transformers
- mistral
- gguf
base_model: gordicaleksa/YugoGPT
model_creator: Gordic Aleksa
model_type: mistral
quantized_by: datatab
---

# YugoGPT-Quantized-GGUF

- **Quantized by:** datatab
- **License:** apache-2.0
- **Author of model :** gordicaleksa/YugoGPT

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## Description

This repo contains YugoGPT - the best open-source base 7B LLM for BCS (Bosnian, Croatian, Serbian) languages developed by Aleksa Gordić.

Eval was computed using https://github.com/gordicaleksa/serbian-llm-eval

It was trained on tens of billions of BCS tokens and is based off of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).

<!-- description end -->

# 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                                                        |