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---
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language:
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license:
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- mistral
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- gguf
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base_model:
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---
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#
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- **
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- **License:** apache-2.0
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- **Finetuned from model :** datatab/Yugo45-GPT
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---
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language:
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- sr
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license: cc
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tags:
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- text-generation-inference
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- transformers
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- mistral
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- gguf
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zero base_model: gordicaleksa/YugoGPT
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model_creator: Gordic Aleksa
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model_type: mistral
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quantized_by: datatab
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datasets:
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- datatab/alpaca-cleaned-serbian-full
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---
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# Yugo45-GPT-Quantized-GGUF
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- **Quantized by:** datatab
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- **License:** apache-2.0
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<!-- description start -->
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## Description
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This repo contains GGUF format model files for [Yugo45-GPT](https://huggingface.co/datatab/Yugo45-GPT).
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<!-- description end -->
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# Quant. preference
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| Quant. | Description |
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|---------------|---------------------------------------------------------------------------------------|
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| not_quantized | Recommended. Fast conversion. Slow inference, big files. |
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| fast_quantized| Recommended. Fast conversion. OK inference, OK file size. |
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| quantized | Recommended. Slow conversion. Fast inference, small files. |
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| f32 | Not recommended. Retains 100% accuracy, but super slow and memory hungry. |
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| f16 | Fastest conversion + retains 100% accuracy. Slow and memory hungry. |
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| q8_0 | Fast conversion. High resource use, but generally acceptable. |
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| q4_k_m | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K |
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| q5_k_m | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K |
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| q2_k | Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.|
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| q3_k_l | Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K |
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| q3_k_m | Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K |
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| q3_k_s | Uses Q3_K for all tensors |
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| q4_0 | Original quant method, 4-bit. |
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| q4_1 | Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.|
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| q4_k_s | Uses Q4_K for all tensors |
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| q4_k | alias for q4_k_m |
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| q5_k | alias for q5_k_m |
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| q5_0 | Higher accuracy, higher resource usage and slower inference. |
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| q5_1 | Even higher accuracy, resource usage and slower inference. |
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| q5_k_s | Uses Q5_K for all tensors |
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| q6_k | Uses Q8_K for all tensors |
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| iq2_xxs | 2.06 bpw quantization |
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| iq2_xs | 2.31 bpw quantization |
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| iq3_xxs | 3.06 bpw quantization |
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| q3_k_xs | 3-bit extra small quantization |
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