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---
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 GGUF format model files for [YugoGPT](https://huggingface.co/gordicaleksa/YugoGPT/).
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# 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 |
|