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YugoGPT-Quantized-GGUF
datatab/YugoGPT-Quantized-GGUF
YugoGPT-Quantized-GGUF is a machine learning model from datatab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
- Quantized by: datatab - License: apache-2.0 - Author of model : gordicaleksa/YugoGPT
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- mistral
- Access
- Public
- Created
- Feb 25, 2024
- Updated
- Feb 26, 2024
- SHA
- f1616017
Base models
Model card
From the Hugging Face model README
YugoGPT-Quantized-GGUF
- Quantized by: datatab
- License: apache-2.0
- Author of model : gordicaleksa/YugoGPT
Description
This repo contains GGUF format model files for YugoGPT.
<!-- 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 |
- Library
- transformers
- Type
- mistral
- License
- apache-2.0
- Languages
- sr
- Created
- Feb 25, 2024
- Updated
- Feb 26, 2024